{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "from bs4 import BeautifulSoup\n",
    "import time\n",
    "import os\n",
    "\n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load house records into dataframe\n",
    "data_column = ['Car_No', 'Car_Price',  'Car_ORC', 'Car_Title', 'Car_Year', 'Car_Brand', 'Car_Model','Car_Location','Car_Mileage(km)',  'Car_Type', 'Car_Gear', 'Car_Seat', 'Car_Fuel(cc)',  'Car_Door_Num',  'Car_Wof_Exp', 'Car_Fuel_Type', 'Car_Reg', 'Car_Color']\n",
    "Car_data_target = pd.read_csv('car_result_FINAL.txt', sep = '|', dtype = 'str', header=None)\n",
    "Car_data_target.columns = data_column \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13442, 18)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check on duplicate values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Wof_Exp</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Reg</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>89038</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2007 Lexus IS250</td>\n",
       "      <td>2007</td>\n",
       "      <td>Lexus</td>\n",
       "      <td>IS250</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>107000</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1342</th>\n",
       "      <td>85695</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2011 Volkswagen Golf 1.4 90Kw</td>\n",
       "      <td>2011</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Golf</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>32800</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>1389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>May 2021</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5533</th>\n",
       "      <td>75514</td>\n",
       "      <td>8995</td>\n",
       "      <td>0</td>\n",
       "      <td>2004 Nissan Murano Full Leather Nice Car</td>\n",
       "      <td>2004</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Murano</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>138452</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>3498</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Car_No Car_Price Car_ORC                                 Car_Title  \\\n",
       "594   89038      9990       0                          2007 Lexus IS250   \n",
       "1342  85695      9990       0             2011 Volkswagen Golf 1.4 90Kw   \n",
       "5533  75514      8995       0  2004 Nissan Murano Full Leather Nice Car   \n",
       "\n",
       "     Car_Year   Car_Brand Car_Model Car_Location Car_Mileage(km)   Car_Type  \\\n",
       "594      2007       Lexus     IS250     Auckland          107000      Sedan   \n",
       "1342     2011  Volkswagen      Golf     Auckland           32800  Hatchback   \n",
       "5533     2004      Nissan    Murano     Auckland          138452     RV/SUV   \n",
       "\n",
       "       Car_Gear Car_Seat Car_Fuel(cc) Car_Door_Num Car_Wof_Exp Car_Fuel_Type  \\\n",
       "594   Tiptronic      NaN         2500          NaN         NaN        Petrol   \n",
       "1342  Automatic        5         1389          NaN         NaN        Petrol   \n",
       "5533  Automatic        5         3498            5         NaN        Petrol   \n",
       "\n",
       "       Car_Reg Car_Color  \n",
       "594        NaN     White  \n",
       "1342  May 2021       Red  \n",
       "5533       NaN      Gold  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target[(Car_data_target.duplicated(['Car_No']) == True)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Wof_Exp</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Reg</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>89038</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2007 Lexus IS250</td>\n",
       "      <td>2007</td>\n",
       "      <td>Lexus</td>\n",
       "      <td>IS250</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>107000</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apr 2022</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>89038</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2007 Lexus IS250</td>\n",
       "      <td>2007</td>\n",
       "      <td>Lexus</td>\n",
       "      <td>IS250</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>107000</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1341</th>\n",
       "      <td>85695</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2011 Volkswagen Golf 1.4 90Kw</td>\n",
       "      <td>2011</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Golf</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>32800</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>1389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mar 2022</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>May 2021</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1342</th>\n",
       "      <td>85695</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2011 Volkswagen Golf 1.4 90Kw</td>\n",
       "      <td>2011</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Golf</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>32800</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>1389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>May 2021</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5532</th>\n",
       "      <td>75514</td>\n",
       "      <td>8995</td>\n",
       "      <td>0</td>\n",
       "      <td>2004 Nissan Murano Full Leather Nice Car</td>\n",
       "      <td>2004</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Murano</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>138452</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>3498</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5533</th>\n",
       "      <td>75514</td>\n",
       "      <td>8995</td>\n",
       "      <td>0</td>\n",
       "      <td>2004 Nissan Murano Full Leather Nice Car</td>\n",
       "      <td>2004</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Murano</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>138452</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>3498</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Car_No Car_Price Car_ORC                                 Car_Title  \\\n",
       "593   89038      9990       0                          2007 Lexus IS250   \n",
       "594   89038      9990       0                          2007 Lexus IS250   \n",
       "1341  85695      9990       0             2011 Volkswagen Golf 1.4 90Kw   \n",
       "1342  85695      9990       0             2011 Volkswagen Golf 1.4 90Kw   \n",
       "5532  75514      8995       0  2004 Nissan Murano Full Leather Nice Car   \n",
       "5533  75514      8995       0  2004 Nissan Murano Full Leather Nice Car   \n",
       "\n",
       "     Car_Year   Car_Brand Car_Model Car_Location Car_Mileage(km)   Car_Type  \\\n",
       "593      2007       Lexus     IS250     Auckland          107000      Sedan   \n",
       "594      2007       Lexus     IS250     Auckland          107000      Sedan   \n",
       "1341     2011  Volkswagen      Golf     Auckland           32800  Hatchback   \n",
       "1342     2011  Volkswagen      Golf     Auckland           32800  Hatchback   \n",
       "5532     2004      Nissan    Murano     Auckland          138452     RV/SUV   \n",
       "5533     2004      Nissan    Murano     Auckland          138452     RV/SUV   \n",
       "\n",
       "       Car_Gear Car_Seat Car_Fuel(cc) Car_Door_Num Car_Wof_Exp Car_Fuel_Type  \\\n",
       "593   Tiptronic      NaN         2500          NaN    Apr 2022        Petrol   \n",
       "594   Tiptronic      NaN         2500          NaN         NaN        Petrol   \n",
       "1341  Automatic        5         1389          NaN    Mar 2022        Petrol   \n",
       "1342  Automatic        5         1389          NaN         NaN        Petrol   \n",
       "5532  Automatic        5         3498            5         NaN        Petrol   \n",
       "5533  Automatic        5         3498            5         NaN        Petrol   \n",
       "\n",
       "       Car_Reg Car_Color  \n",
       "593        NaN     White  \n",
       "594        NaN     White  \n",
       "1341  May 2021       Red  \n",
       "1342  May 2021       Red  \n",
       "5532       NaN      Gold  \n",
       "5533       NaN      Gold  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target[(Car_data_target['Car_No'] == '89038') | (Car_data_target['Car_No'] == '85695') | (Car_data_target['Car_No'] == '75514')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop duplicate records "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target = Car_data_target.drop(Car_data_target[(Car_data_target['Car_No'] == '89038') & (Car_data_target['Car_Wof_Exp'].isnull() == True)].index)\n",
    "Car_data_target = Car_data_target.drop(Car_data_target[(Car_data_target['Car_No'] == '85695') & (Car_data_target['Car_Wof_Exp'].isnull() == True)].index)\n",
    "Car_data_target.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13439, 18)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Wof_Exp</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Reg</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Car_No, Car_Price, Car_ORC, Car_Title, Car_Year, Car_Brand, Car_Model, Car_Location, Car_Mileage(km), Car_Type, Car_Gear, Car_Seat, Car_Fuel(cc), Car_Door_Num, Car_Wof_Exp, Car_Fuel_Type, Car_Reg, Car_Color]\n",
       "Index: []"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target[(Car_data_target.duplicated(['Car_No']) == True)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check null value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_No                 0\n",
       "Car_Price            863\n",
       "Car_ORC                0\n",
       "Car_Title              0\n",
       "Car_Year               0\n",
       "Car_Brand              0\n",
       "Car_Model             91\n",
       "Car_Location           0\n",
       "Car_Mileage(km)        0\n",
       "Car_Type               1\n",
       "Car_Gear              33\n",
       "Car_Seat            2645\n",
       "Car_Fuel(cc)         725\n",
       "Car_Door_Num        2531\n",
       "Car_Wof_Exp        10634\n",
       "Car_Fuel_Type        496\n",
       "Car_Reg            12889\n",
       "Car_Color           1608\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Missing value percentage for each columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Reg            0.959074\n",
       "Car_Wof_Exp        0.791279\n",
       "Car_Seat           0.196815\n",
       "Car_Door_Num       0.188332\n",
       "Car_Color          0.119652\n",
       "Car_Price          0.064216\n",
       "Car_Fuel(cc)       0.053947\n",
       "Car_Fuel_Type      0.036908\n",
       "Car_Model          0.006771\n",
       "Car_Gear           0.002456\n",
       "Car_Type           0.000074\n",
       "Car_Title          0.000000\n",
       "Car_ORC            0.000000\n",
       "Car_Mileage(km)    0.000000\n",
       "Car_Year           0.000000\n",
       "Car_Brand          0.000000\n",
       "Car_Location       0.000000\n",
       "Car_No             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = (Car_data_target.isnull().sum()/len(Car_data_target))\n",
    "missing.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop columns which missing percentage larger than 50%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop columns which missing percentage larger than 50% \n",
    "# drop Car_Reg and Wof_Exp \n",
    "Car_data_target.drop(['Car_Reg'], axis=1, inplace=True)\n",
    "\n",
    "Car_data_target.drop(['Car_Wof_Exp'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assign Car_Seat and Car_Door_Num according to Car_Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      NaN\n",
       "2        5\n",
       "6        4\n",
       "63       2\n",
       "122      3\n",
       "Name: Car_Door_Num, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target['Car_Door_Num'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                5\n",
       "1                7\n",
       "4                8\n",
       "18             NaN\n",
       "31               4\n",
       "42               3\n",
       "46               2\n",
       "425              6\n",
       "1536    10 or more\n",
       "Name: Car_Seat, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target['Car_Seat'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target['Car_Seat'] = Car_data_target['Car_Seat'].replace('10 or more', '10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Car_data_target[(Car_data_target['Car_Seat'] == '10 or more')]\n",
    "\n",
    "Car_data_target['Car_Seat'].fillna(value = 0, inplace=True)\n",
    "Car_data_target['Car_Door_Num'].fillna(value = 0, inplace=True)\n",
    "Car_data_target['Car_Fuel(cc)'].fillna(value = 0, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5\n",
       "1        7\n",
       "4        8\n",
       "18       0\n",
       "31       4\n",
       "42       3\n",
       "46       2\n",
       "425      6\n",
       "1536    10\n",
       "Name: Car_Seat, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target['Car_Seat'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86261</td>\n",
       "      <td>3997</td>\n",
       "      <td>0</td>\n",
       "      <td>2005 Nissan Tiida Axis</td>\n",
       "      <td>2005</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Tiida</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>138150</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79395</td>\n",
       "      <td>7997</td>\n",
       "      <td>0</td>\n",
       "      <td>2009 Toyota Wish</td>\n",
       "      <td>2009</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Wish</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>100300</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7</td>\n",
       "      <td>1800</td>\n",
       "      <td>0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85473</td>\n",
       "      <td>9940</td>\n",
       "      <td>1</td>\n",
       "      <td>2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...</td>\n",
       "      <td>2007</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>102425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>2400</td>\n",
       "      <td>5</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88342</td>\n",
       "      <td>8940</td>\n",
       "      <td>1</td>\n",
       "      <td>2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...</td>\n",
       "      <td>2006</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>109425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>2400</td>\n",
       "      <td>5</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72923</td>\n",
       "      <td>9900</td>\n",
       "      <td>1</td>\n",
       "      <td>2012 Nissan Serena 20X</td>\n",
       "      <td>2012</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Serena</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>107126</td>\n",
       "      <td>Van</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>8</td>\n",
       "      <td>2000</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Light Blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_No Car_Price Car_ORC                                          Car_Title  \\\n",
       "0  86261      3997       0                             2005 Nissan Tiida Axis   \n",
       "1  79395      7997       0                                   2009 Toyota Wish   \n",
       "2  85473     9940        1  2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...   \n",
       "3  88342     8940        1  2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...   \n",
       "4  72923     9900        1                             2012 Nissan Serena 20X   \n",
       "\n",
       "  Car_Year   Car_Brand  Car_Model Car_Location Car_Mileage(km)       Car_Type  \\\n",
       "0     2005      Nissan      Tiida      Dunedin          138150      Hatchback   \n",
       "1     2009      Toyota       Wish      Dunedin          100300  Station Wagon   \n",
       "2     2007  Mitsubishi  OUTLANDER     Auckland          102425         RV/SUV   \n",
       "3     2006  Mitsubishi  OUTLANDER     Auckland          109425         RV/SUV   \n",
       "4     2012      Nissan     Serena     Auckland          107126            Van   \n",
       "\n",
       "    Car_Gear Car_Seat Car_Fuel(cc) Car_Door_Num Car_Fuel_Type   Car_Color  \n",
       "0  Automatic        5         1500            0        Petrol      Silver  \n",
       "1  Automatic        7         1800            0        Petrol       White  \n",
       "2  Automatic        5         2400            5        Petrol      Silver  \n",
       "3  Automatic        5         2400            5        Petrol       Black  \n",
       "4  Automatic        8         2000            5           NaN  Light Blue  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tiida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>TIIDA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4145</th>\n",
       "      <td>tiida</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Car_Model\n",
       "0        Tiida\n",
       "75       TIIDA\n",
       "4145     tiida"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target[['Car_Model']][(Car_data_target['Car_Model'].str.upper() == 'TIIDA')].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert data type from object to int\n",
    "Car_data_target['Car_Seat'] = Car_data_target['Car_Seat'].astype(int)\n",
    "\n",
    "Car_data_target['Car_Door_Num'] = Car_data_target['Car_Door_Num'].astype(int)\n",
    "\n",
    "Car_data_target['Car_Fuel(cc)'] = Car_data_target['Car_Fuel(cc)'].astype(int)\n",
    "\n",
    "Car_data_target['Car_Fuel(cc)'] = Car_data_target['Car_Fuel(cc)'].astype(int)\n",
    "\n",
    "\n",
    "Car_data_target['Car_Model_upper'] = Car_data_target['Car_Model'].str.upper()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Get median door number and seat for each brand and model\n",
    "\n",
    "Car_median_seat = Car_data_target[(Car_data_target['Car_Seat'] != 0)].groupby(['Car_Brand', 'Car_Model_upper', 'Car_Type'])[['Car_Seat']].median().reset_index()\n",
    "\n",
    "Car_median_door = Car_data_target[(Car_data_target['Car_Door_Num'] != 0)].groupby(['Car_Brand', 'Car_Model_upper', 'Car_Type'])[['Car_Door_Num']].median().reset_index()\n",
    "\n",
    "Car_median_fuel = Car_data_target[(Car_data_target['Car_Fuel(cc)'] != 0)].groupby(['Car_Brand', 'Car_Model_upper', 'Car_Type'])[['Car_Fuel(cc)']].median().reset_index()\n",
    "  \n",
    "# Convert to integer format\n",
    "    \n",
    "Car_median_seat['Car_Seat'] = Car_median_seat['Car_Seat'].astype(int)\n",
    "\n",
    "Car_median_door['Car_Door_Num'] = Car_median_door['Car_Door_Num'].astype(int)\n",
    "\n",
    "Car_median_fuel['Car_Fuel(cc)'] = Car_median_fuel['Car_Fuel(cc)'].astype(int)\n",
    " \n",
    "#,'Car_Seat', 'Car_Door_Num']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model_upper</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aston</td>\n",
       "      <td>MARTIN</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>4735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aston</td>\n",
       "      <td>MARTIN</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Audi</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>1.8T</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>1798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Brand Car_Model_upper       Car_Type  Car_Fuel(cc)\n",
       "0     Aston          MARTIN          Coupe          4735\n",
       "1     Aston          MARTIN          Sedan          6000\n",
       "2      Audi             1.8          Sedan          1800\n",
       "3      Audi            1.8T  Station Wagon          1798\n",
       "4      Audi             2.0          Coupe          1984"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_median_fuel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1506 entries, 0 to 1505\n",
      "Data columns (total 4 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Car_Brand        1506 non-null   object\n",
      " 1   Car_Model_upper  1506 non-null   object\n",
      " 2   Car_Type         1506 non-null   object\n",
      " 3   Car_Door_Num     1506 non-null   int32 \n",
      "dtypes: int32(1), object(3)\n",
      "memory usage: 41.3+ KB\n"
     ]
    }
   ],
   "source": [
    "Car_median_door.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target_seat = pd.merge(Car_data_target, Car_median_seat, on=['Car_Brand', 'Car_Model_upper', 'Car_Type'], how = 'left')\n",
    "\n",
    "# Assign median seat number if seat number is null \n",
    "Car_data_target_seat.loc[Car_data_target_seat['Car_Seat_x'] == 0, 'Car_Seat_x'] = Car_data_target_seat.loc[Car_data_target_seat['Car_Seat_x'] == 0, 'Car_Seat_y']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18       5.0\n",
       "20       7.0\n",
       "33       7.0\n",
       "37       5.0\n",
       "44       NaN\n",
       "        ... \n",
       "13386    5.0\n",
       "13390    5.0\n",
       "13392    4.0\n",
       "13416    5.0\n",
       "13434    7.0\n",
       "Name: Car_Seat_x, Length: 2645, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat.loc[Car_data_target[(Car_data_target['Car_Seat'] == 0)].index, 'Car_Seat_x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18       5.0\n",
       "20       7.0\n",
       "33       7.0\n",
       "37       5.0\n",
       "44       NaN\n",
       "        ... \n",
       "13386    5.0\n",
       "13390    5.0\n",
       "13392    4.0\n",
       "13416    5.0\n",
       "13434    7.0\n",
       "Name: Car_Seat_y, Length: 2645, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat.loc[Car_data_target[(Car_data_target['Car_Seat'] == 0)].index, 'Car_Seat_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target_seat_door = pd.merge(Car_data_target_seat, Car_median_door, on=['Car_Brand', 'Car_Model_upper', 'Car_Type'], how = 'left')\n",
    "\n",
    "# Assign median door number if seat number is null \n",
    "Car_data_target_seat_door.loc[Car_data_target[(Car_data_target['Car_Door_Num'] == 0)].index, 'Car_Door_Num_x'] = Car_data_target_seat_door.loc[Car_data_target[(Car_data_target['Car_Door_Num'] == 0)].index, 'Car_Door_Num_y']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13439, 19)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5.0\n",
       "1        5.0\n",
       "20       5.0\n",
       "33       5.0\n",
       "35       4.0\n",
       "        ... \n",
       "13400    2.0\n",
       "13406    4.0\n",
       "13413    5.0\n",
       "13416    5.0\n",
       "13434    5.0\n",
       "Name: Car_Door_Num_x, Length: 2531, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door.loc[Car_data_target[(Car_data_target['Car_Door_Num'] == 0)].index, 'Car_Door_Num_x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5.0\n",
       "1        5.0\n",
       "20       5.0\n",
       "33       5.0\n",
       "35       4.0\n",
       "        ... \n",
       "13400    2.0\n",
       "13406    4.0\n",
       "13413    5.0\n",
       "13416    5.0\n",
       "13434    5.0\n",
       "Name: Car_Door_Num_y, Length: 2531, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door.loc[Car_data_target[(Car_data_target['Car_Door_Num'] == 0)].index, 'Car_Door_Num_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target_seat_door_fuel = pd.merge(Car_data_target_seat_door, Car_median_fuel, on=['Car_Brand', 'Car_Model_upper', 'Car_Type'], how = 'left')\n",
    "\n",
    "# Assign median door number if seat number is null \n",
    "Car_data_target_seat_door_fuel.loc[Car_data_target[(Car_data_target['Car_Fuel(cc)'] == 0)].index, 'Car_Fuel(cc)_x'] = Car_data_target_seat_door_fuel.loc[Car_data_target[(Car_data_target['Car_Fuel(cc)'] == 0)].index, 'Car_Fuel(cc)_y']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44          NaN\n",
       "127         NaN\n",
       "138      2350.0\n",
       "167      1240.0\n",
       "218      2400.0\n",
       "          ...  \n",
       "13371    1500.0\n",
       "13372    1800.0\n",
       "13374    1500.0\n",
       "13390    3498.0\n",
       "13396    2000.0\n",
       "Name: Car_Fuel(cc)_x, Length: 725, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.loc[Car_data_target[(Car_data_target['Car_Fuel(cc)'] == 0)].index, 'Car_Fuel(cc)_x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44          NaN\n",
       "127         NaN\n",
       "138      2350.0\n",
       "167      1240.0\n",
       "218      2400.0\n",
       "          ...  \n",
       "13371    1500.0\n",
       "13372    1800.0\n",
       "13374    1500.0\n",
       "13390    3498.0\n",
       "13396    2000.0\n",
       "Name: Car_Fuel(cc)_y, Length: 725, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.loc[Car_data_target[(Car_data_target['Car_Fuel(cc)'] == 0)].index, 'Car_Fuel(cc)_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_columns = ['Car_No', 'Car_Price', 'Car_ORC', 'Car_Title', 'Car_Year', 'Car_Brand',\n",
    "       'Car_Model', 'Car_Location', 'Car_Mileage(km)', 'Car_Type', 'Car_Gear',\n",
    "       'Car_Seat', 'Car_Fuel(cc)', 'Car_Door_Num', 'Car_Fuel_Type',\n",
    "       'Car_Color']\n",
    "Car_data_target_seat_door_fuel.drop(['Car_Seat_y', 'Car_Door_Num_y', 'Car_Model_upper', 'Car_Fuel(cc)_y'], axis=1, inplace=True) \n",
    "Car_data_target_seat_door_fuel.columns = data_columns \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86261</td>\n",
       "      <td>3997</td>\n",
       "      <td>0</td>\n",
       "      <td>2005 Nissan Tiida Axis</td>\n",
       "      <td>2005</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Tiida</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>138150</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79395</td>\n",
       "      <td>7997</td>\n",
       "      <td>0</td>\n",
       "      <td>2009 Toyota Wish</td>\n",
       "      <td>2009</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Wish</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>100300</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85473</td>\n",
       "      <td>9940</td>\n",
       "      <td>1</td>\n",
       "      <td>2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...</td>\n",
       "      <td>2007</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>102425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88342</td>\n",
       "      <td>8940</td>\n",
       "      <td>1</td>\n",
       "      <td>2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...</td>\n",
       "      <td>2006</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>109425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72923</td>\n",
       "      <td>9900</td>\n",
       "      <td>1</td>\n",
       "      <td>2012 Nissan Serena 20X</td>\n",
       "      <td>2012</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Serena</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>107126</td>\n",
       "      <td>Van</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Light Blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_No Car_Price Car_ORC                                          Car_Title  \\\n",
       "0  86261      3997       0                             2005 Nissan Tiida Axis   \n",
       "1  79395      7997       0                                   2009 Toyota Wish   \n",
       "2  85473     9940        1  2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...   \n",
       "3  88342     8940        1  2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...   \n",
       "4  72923     9900        1                             2012 Nissan Serena 20X   \n",
       "\n",
       "  Car_Year   Car_Brand  Car_Model Car_Location Car_Mileage(km)       Car_Type  \\\n",
       "0     2005      Nissan      Tiida      Dunedin          138150      Hatchback   \n",
       "1     2009      Toyota       Wish      Dunedin          100300  Station Wagon   \n",
       "2     2007  Mitsubishi  OUTLANDER     Auckland          102425         RV/SUV   \n",
       "3     2006  Mitsubishi  OUTLANDER     Auckland          109425         RV/SUV   \n",
       "4     2012      Nissan     Serena     Auckland          107126            Van   \n",
       "\n",
       "    Car_Gear  Car_Seat  Car_Fuel(cc)  Car_Door_Num Car_Fuel_Type   Car_Color  \n",
       "0  Automatic       5.0        1500.0           5.0        Petrol      Silver  \n",
       "1  Automatic       7.0        1800.0           5.0        Petrol       White  \n",
       "2  Automatic       5.0        2400.0           5.0        Petrol      Silver  \n",
       "3  Automatic       5.0        2400.0           5.0        Petrol       Black  \n",
       "4  Automatic       8.0        2000.0           5.0           NaN  Light Blue  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2997.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>1990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1147</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1671</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>1998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2455</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2708</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3804</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4265</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4448</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2379.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4562</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9542</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11500</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2356.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12776</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Car_Brand  Car_Model       Car_Type  Car_Fuel(cc)\n",
       "2      Mitsubishi  OUTLANDER         RV/SUV        2400.0\n",
       "64     Mitsubishi  Outlander         RV/SUV        2400.0\n",
       "85     Mitsubishi  Outlander         RV/SUV        2359.0\n",
       "175    Mitsubishi  Outlander  Station Wagon        2359.0\n",
       "273    Mitsubishi  Outlander  Station Wagon        2997.0\n",
       "331    Mitsubishi  Outlander         RV/SUV        2352.0\n",
       "592    Mitsubishi  Outlander         RV/SUV        1990.0\n",
       "723    Mitsubishi  OUTLANDER  Station Wagon        2400.0\n",
       "1049   Mitsubishi  Outlander         RV/SUV           0.0\n",
       "1147   Mitsubishi  Outlander         RV/SUV        2000.0\n",
       "1671   Mitsubishi  Outlander  Station Wagon        1998.0\n",
       "2156   Mitsubishi  Outlander         RV/SUV        2300.0\n",
       "2455   Mitsubishi  Outlander  Station Wagon           0.0\n",
       "2708   Mitsubishi  Outlander         RV/SUV        2350.0\n",
       "3804   Mitsubishi  Outlander         RV/SUV        2360.0\n",
       "4265   Mitsubishi  Outlander  Station Wagon        2400.0\n",
       "4448   Mitsubishi  Outlander  Station Wagon        2379.0\n",
       "4562   Mitsubishi  Outlander  Station Wagon        2350.0\n",
       "9542   Mitsubishi  OUTLANDER         RV/SUV        2350.0\n",
       "11500  Mitsubishi  OUTLANDER         RV/SUV        2356.0\n",
       "12776  Mitsubishi  OUTLANDER         RV/SUV           0.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Car_data_target_seat_door.groupby(['Car_Brand', 'Car_Model', 'Car_Type'])[['Car_Fuel(cc)']]\n",
    "\n",
    "Car_data_target_seat_door_fuel[['Car_Brand', 'Car_Model', 'Car_Type', 'Car_Fuel(cc)']][(Car_data_target_seat_door['Car_Model'].str.upper() == 'OUTLANDER')].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13439 entries, 0 to 13438\n",
      "Data columns (total 16 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Car_No           13439 non-null  object \n",
      " 1   Car_Price        12576 non-null  object \n",
      " 2   Car_ORC          13439 non-null  object \n",
      " 3   Car_Title        13439 non-null  object \n",
      " 4   Car_Year         13439 non-null  object \n",
      " 5   Car_Brand        13439 non-null  object \n",
      " 6   Car_Model        13348 non-null  object \n",
      " 7   Car_Location     13439 non-null  object \n",
      " 8   Car_Mileage(km)  13439 non-null  object \n",
      " 9   Car_Type         13438 non-null  object \n",
      " 10  Car_Gear         13406 non-null  object \n",
      " 11  Car_Seat         13159 non-null  float64\n",
      " 12  Car_Fuel(cc)     13410 non-null  float64\n",
      " 13  Car_Door_Num     13351 non-null  float64\n",
      " 14  Car_Fuel_Type    12943 non-null  object \n",
      " 15  Car_Color        11831 non-null  object \n",
      "dtypes: float64(3), object(13)\n",
      "memory usage: 2.4+ MB\n"
     ]
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Color          0.119652\n",
       "Car_Price          0.064216\n",
       "Car_Fuel_Type      0.036908\n",
       "Car_Seat           0.020835\n",
       "Car_Model          0.006771\n",
       "Car_Door_Num       0.006548\n",
       "Car_Gear           0.002456\n",
       "Car_Fuel(cc)       0.002158\n",
       "Car_Type           0.000074\n",
       "Car_Mileage(km)    0.000000\n",
       "Car_Location       0.000000\n",
       "Car_Brand          0.000000\n",
       "Car_Year           0.000000\n",
       "Car_Title          0.000000\n",
       "Car_ORC            0.000000\n",
       "Car_No             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = (Car_data_target_seat_door_fuel.isnull().sum()/len(Car_data_target_seat_door))\n",
    "missing.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop rows with null price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target_seat_door_fuel.dropna(axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Color          0.0\n",
       "Car_Fuel_Type      0.0\n",
       "Car_Door_Num       0.0\n",
       "Car_Fuel(cc)       0.0\n",
       "Car_Seat           0.0\n",
       "Car_Gear           0.0\n",
       "Car_Type           0.0\n",
       "Car_Mileage(km)    0.0\n",
       "Car_Location       0.0\n",
       "Car_Model          0.0\n",
       "Car_Brand          0.0\n",
       "Car_Year           0.0\n",
       "Car_Title          0.0\n",
       "Car_ORC            0.0\n",
       "Car_Price          0.0\n",
       "Car_No             0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = (Car_data_target_seat_door_fuel.isnull().sum()/len(Car_data_target_seat_door))\n",
    "missing.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10360, 16)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete records with P.O.A price\n",
    "Car_data_target_seat_door_fuel.drop(Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Price'] == 'P.0.A')].index, axis=0, inplace = True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Color          0.0\n",
       "Car_Fuel_Type      0.0\n",
       "Car_Door_Num       0.0\n",
       "Car_Fuel(cc)       0.0\n",
       "Car_Seat           0.0\n",
       "Car_Gear           0.0\n",
       "Car_Type           0.0\n",
       "Car_Mileage(km)    0.0\n",
       "Car_Location       0.0\n",
       "Car_Model          0.0\n",
       "Car_Brand          0.0\n",
       "Car_Year           0.0\n",
       "Car_Title          0.0\n",
       "Car_ORC            0.0\n",
       "Car_Price          0.0\n",
       "Car_No             0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = (Car_data_target_seat_door_fuel.isnull().sum()/len(Car_data_target_seat_door))\n",
    "missing.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to integer for numeric columns \n",
    "Car_data_target_seat_door_fuel['Car_Price'] = Car_data_target_seat_door_fuel['Car_Price'].astype('int')\n",
    "Car_data_target_seat_door_fuel['Car_Year'] = Car_data_target_seat_door_fuel['Car_Year'].astype('int')\n",
    "Car_data_target_seat_door_fuel['Car_Mileage(km)'] = Car_data_target_seat_door_fuel['Car_Mileage(km)'].astype('int')\n",
    "Car_data_target_seat_door_fuel['Car_Fuel(cc)'] = Car_data_target_seat_door_fuel['Car_Fuel(cc)'].astype('int')\n",
    "Car_data_target_seat_door_fuel['Car_Price'] = Car_data_target_seat_door_fuel['Car_Price'].astype('int')\n",
    "Car_data_target_seat_door_fuel['Car_ORC'] = Car_data_target_seat_door_fuel['Car_ORC'].astype('int')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10069, 16)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10069 entries, 0 to 13438\n",
      "Data columns (total 16 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Car_No           10069 non-null  object \n",
      " 1   Car_Price        10069 non-null  int32  \n",
      " 2   Car_ORC          10069 non-null  int32  \n",
      " 3   Car_Title        10069 non-null  object \n",
      " 4   Car_Year         10069 non-null  int32  \n",
      " 5   Car_Brand        10069 non-null  object \n",
      " 6   Car_Model        10069 non-null  object \n",
      " 7   Car_Location     10069 non-null  object \n",
      " 8   Car_Mileage(km)  10069 non-null  int32  \n",
      " 9   Car_Type         10069 non-null  object \n",
      " 10  Car_Gear         10069 non-null  object \n",
      " 11  Car_Seat         10069 non-null  float64\n",
      " 12  Car_Fuel(cc)     10069 non-null  int32  \n",
      " 13  Car_Door_Num     10069 non-null  float64\n",
      " 14  Car_Fuel_Type    10069 non-null  object \n",
      " 15  Car_Color        10069 non-null  object \n",
      "dtypes: float64(2), int32(5), object(9)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\env\\ad\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Car Fuel(cc)')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Car_data_target_seat_door_fuel['Car_Fuel(cc)'])\n",
    "plt.title('Distribution of Car Fuel(cc)', fontsize = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Delete Outlier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3999</th>\n",
       "      <td>76494</td>\n",
       "      <td>6950</td>\n",
       "      <td>1</td>\n",
       "      <td>2006 Honda Odyssey</td>\n",
       "      <td>2006</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Odyssey</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>111889</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7.0</td>\n",
       "      <td>111889</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4006</th>\n",
       "      <td>76004</td>\n",
       "      <td>17495</td>\n",
       "      <td>1</td>\n",
       "      <td>2011 Audi Cabriolet</td>\n",
       "      <td>2011</td>\n",
       "      <td>Audi</td>\n",
       "      <td>Cabriolet</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>57325</td>\n",
       "      <td>Convertible</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Grey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10963</th>\n",
       "      <td>31643</td>\n",
       "      <td>9440</td>\n",
       "      <td>1</td>\n",
       "      <td>2008 Toyota Auris</td>\n",
       "      <td>2008</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Auris</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>98000</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>98000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12884</th>\n",
       "      <td>23440</td>\n",
       "      <td>12990</td>\n",
       "      <td>1</td>\n",
       "      <td>2008 Toyota Harrier</td>\n",
       "      <td>2008</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Harrier</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>97500</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>23600</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Car_No  Car_Price  Car_ORC            Car_Title  Car_Year Car_Brand  \\\n",
       "3999   76494       6950        1   2006 Honda Odyssey      2006     Honda   \n",
       "4006   76004      17495        1  2011 Audi Cabriolet      2011      Audi   \n",
       "10963  31643       9440        1    2008 Toyota Auris      2008    Toyota   \n",
       "12884  23440      12990        1  2008 Toyota Harrier      2008    Toyota   \n",
       "\n",
       "       Car_Model Car_Location  Car_Mileage(km)       Car_Type   Car_Gear  \\\n",
       "3999     Odyssey     Auckland           111889  Station Wagon  Automatic   \n",
       "4006   Cabriolet     Auckland            57325    Convertible  Tiptronic   \n",
       "10963      Auris     Auckland            98000      Hatchback  Automatic   \n",
       "12884    Harrier     Auckland            97500         RV/SUV  Automatic   \n",
       "\n",
       "       Car_Seat  Car_Fuel(cc)  Car_Door_Num Car_Fuel_Type Car_Color  \n",
       "3999        7.0        111889           5.0        Petrol     White  \n",
       "4006        4.0         20000           2.0        Petrol      Grey  \n",
       "10963       5.0         98000           5.0        Petrol       Red  \n",
       "12884       5.0         23600           4.0        Petrol     Black  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Fuel(cc)'] > 10000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete 76004\n",
    "\n",
    "Car_data_target_seat_door_fuel.drop(Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_No'] == '76004')].index, axis=0, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_Odyssey = Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Model'] == 'Odyssey') & (Car_data_target_seat_door_fuel['Car_Type'] == 'Station Wagon') & (Car_data_target_seat_door_fuel['Car_Brand'] == 'Honda')].groupby(['Car_Model', 'Car_Type', 'Car_Brand'])[['Car_Fuel(cc)']].median().reset_index()\n",
    "\n",
    "Car_data_target_seat_door_fuel.loc[Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_No'] == '76494')].index, 'Car_Fuel(cc)'] = median_Odyssey.loc[0, 'Car_Fuel(cc)']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_Auris = Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Model'] == 'Auris') & (Car_data_target_seat_door_fuel['Car_Type'] == 'Hatchback') & (Car_data_target_seat_door_fuel['Car_Brand'] == 'Toyota')].groupby(['Car_Model', 'Car_Type'])[['Car_Fuel(cc)']].median().reset_index()\n",
    "Car_data_target_seat_door_fuel.loc[Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_No'] == '31643')].index, 'Car_Fuel(cc)'] = median_Auris.loc[0, 'Car_Fuel(cc)']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_Harrier = Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Model'] == 'Harrier') & (Car_data_target_seat_door_fuel['Car_Type'] == 'RV/SUV') & (Car_data_target_seat_door_fuel['Car_Brand'] == 'Toyota')].groupby(['Car_Model', 'Car_Type', 'Car_Brand'])[['Car_Fuel(cc)']].median().reset_index()\n",
    "Car_data_target_seat_door_fuel.loc[Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_No'] == '23440')].index, 'Car_Fuel(cc)'] = median_Harrier.loc[0, 'Car_Fuel(cc)']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\env\\ad\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Car Fuel(cc)')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FmpNxIMYYY4aXsoDi+kRuAp4BNgHLVHWDiNwhIpe6bA8AZSJSB9wM3OK23QAsAzYCTwM3qqofQER+BbwEzBORBhFZ4sp6EDjBDSdeClzngpMxxphRIMfzd25NTY3W1tamuxpjns3lZczYIiKvqGpNaPpY6pQ3xhiTRhZQjDHGJIUFFGOMMUlhAcUYY0xSWEAxxhiTFBZQjDHGJIUFFGOMMUlhAcUYY0xSWEAxxhiTFBZQjDHGJIUFFGOMMUlhAcUYY0xSZKe7AsbEItwEkza5pDGZxc5QjDHGJIUFFGOMMUlhAcUYY0xSpDSgiMgiEdkiInUickuY9Xki8qhbv1pEqoPW3erSt4jIxUHpD4pIo7szY7h9/rOIqIjY/eSNMWYUpSygiIgPuAe4BJgPXC0i80OyLQEOqOoc4G7gLrftfLx70C8AFgH3uvIAHnJp4fY5HfggEP4WgcYYY1ImlWcoZwN1qrpdVXvx7vO+OCTPYuBht/w4cJGIiEtfqqo9qroDqHPloarPA60R9nk38DXg+L2vsTHGpEkqA0olsDvodYNLC5tHVfuBNqAsxm0HEZHFwB5VXTdMvhtEpFZEapuammI5DmOMMTEYE53yIjIO+FfgtuHyqup9qlqjqjUVFRWpr5wxxhwnUhlQ9gDTg15XubSweUQkGygBWmLcNthsYBawTkTqXf5XRWTKCOpvjDEmDqkMKGuBuSIyS0Ry8TrZl4fkWQ5c55YvB1aqqrr0q9wosFnAXGBNpB2p6puqOklVq1W1Gq+J7AxV3Z/cQzLGGBNJygKK6xO5CXgG2AQsU9UNInKHiFzqsj0AlIlIHXAzcIvbdgOwDNgIPA3cqKp+ABH5FfASME9EGkRkSaqOwRhjTOxSOpeXqq4AVoSk3Ra03A1cEWHbO4E7w6RfHcN+q+OtqzHGmJEZE53yxhhj0s8CijHGmKSwgGKMMSYpLKAYY4xJCgsoxhhjksICijHGmKSwgGKMMSYpLKAYY4xJCgsoxhhjksICijHGmKSwgGKMMSYpLKAYY4xJCgsoxhhjksICijHGmKSwgGKMMSYpLKAYY4xJipQGFBFZJCJbRKRORG4Jsz5PRB5161eLSHXQultd+hYRuTgo/UERaRSR9SFl/UBENovIGyLypIiUpvLYjDHGDJaygCIiPuAe4BJgPnC1iMwPybYEOKCqc4C7gbvctvPx7kG/AFgE3OvKA3jIpYX6E3CKqp4KvAXcmtQDMsYYE1Uqz1DOBupUdbuq9gJLgcUheRYDD7vlx4GLRERc+lJV7VHVHUCdKw9VfR5oDd2Zqv7R3cce4GWgKtkHZIwxJrJUBpRKYHfQ6waXFjaPCwZtQFmM20bzaeAP4VaIyA0iUisitU1NTXEUaYwxJpox1ykvIl8H+oFfhFuvqvepao2q1lRUVIxu5YwxZgxLZUDZA0wPel3l0sLmEZFsoARoiXHbIUTkeuAjwCdUVROtuDHGmPilMqCsBeaKyCwRycXrZF8ekmc5cJ1bvhxY6QLBcuAqNwpsFjAXWBNtZyKyCPgacKmqdibxOIwxxsQgZQHF9YncBDwDbAKWqeoGEblDRC512R4AykSkDrgZuMVtuwFYBmwEngZuVFU/gIj8CngJmCciDSKyxJX1v0Ax8CcReV1EfpyqYzPGGDNUdioLV9UVwIqQtNuClruBKyJseydwZ5j0qyPknzOiyhpjjBmRMdcpb4wxJj0soBhjjEkKCyjGGGOSwgKKMcaYpLCAYowxJiksoBhjjEkKCyjGGGOSwgKKMcaYpLCAYowxJiksoBhjjEkKCyjGGGOSwgKKMcaYpLCAYowxJiksoBhjjEkKCyjGGGOSwgKKMcaYpEhpQBGRRSKyRUTqROSWMOvzRORRt361iFQHrbvVpW8RkYuD0h8UkUYRWR9S1kQR+ZOIbHXPE1J5bMYYYwZLWUARER9wD3AJMB+4WkTmh2RbAhxwd1u8G7jLbTsf7x70C4BFwL2uPICHXFqoW4DnVHUu8Jx7bYwxZpSk8gzlbKBOVberai+wFFgckmcx8LBbfhy4SETEpS9V1R5V3QHUufJQ1eeB1jD7Cy7rYeDvkngsxhhjhhFTQBGRX4vIh0UkngBUCewOet3g0sLmUdV+oA0oi3HbUJNVdZ9b3g9MDpdJRG4QkVoRqW1qaorlOIwxxsQg1gBxL3ANsFVEvi8i81JYpxFTVQU0wrr7VLVGVWsqKipGuWbGGDN2xRRQVPVZVf0EcAZQDzwrIi+KyKdEJCfCZnuA6UGvq1xa2Dwikg2UAC0xbhvqbRGZ6sqaCjQOd1zGGGOSJ+YmLBEpA64HPgO8BvwXXoD5U4RN1gJzRWSWiOTidbIvD8mzHLjOLV8OrHRnF8uBq9wosFnAXGDNMFUMLus64DcxHpoxxpgkyI4lk4g8CcwDHgE+GtRX8aiI1IbbRlX7ReQm4BnABzyoqhtE5A6gVlWXAw8Aj4hIHV5H+1Vu2w0isgzYCPQDN6qq39XlV8AFQLmINADfVNUHgO8Dy0RkCbAT+HicfwtjjDEjEFNAAX6qqiuCE0Qkz43Cqom0kdtmRUjabUHL3cAVEba9E7gzTPrVEfK3ABdFOwhjjDGpE2uT13fCpL2UzIoYY4w5tkU9QxGRKXjDdQtE5HRA3KrxwLgU180YY8wxZLgmr4vxOuKrgP8MSj8M/GuK6mSMMeYYFDWgqOrDwMMi8jFVfWKU6mSMMeYYNFyT17Wq+v+AahG5OXS9qv5nmM2MMcYch4Zr8ip0z0Wprogxxphj23BNXj9xz98aneoYY4w5VsU6OeS/i8h4EckRkedEpElErk115Ywxxhw7Yr0O5YOqegj4CN5cXnOAr6aqUsYYY449sQaUQNPYh4HHVLUtRfUxY0x3n5+Onv50V8MYMwpiDSi/E5HNwJnAcyJSAXSnrlpmLFBVPvWztdz97Fu0dfWluzrGmBSLdfr6W4DzgRpV7QM6GHr3RWMGeXr9fl7a3kJnr5/HX9nNgIa9RY0xZoyIdXJIgJPwrkcJ3ubnSa6PGSN6+v189w+bOGlKMfOmFPOb1/fy5p42FlaVprtqxpgUiXWU1yPAfwDvAs5yj4izDBuzZkcru1u7uPkDJ3JW9UQKcnxsb2pPd7WMMSkU6xlKDTDf3fzKmGGt3dFKlsD5c8pZ/vpeZkwcx86WznRXyxiTQrF2yq8HpqSyImZsWVPfyoJpJRTleb9ZZpSNo/FwD129/jTXzBiTKrGeoZQDG0VkDdATSFTVS6NtJCKL8G4V7APuV9Xvh6zPw+uHORPvXvJXqmq9W3crsATwA19U1WeilSkiFwE/wAuS7cD1qloX4/GZJOrtH+C1XQe55pwZR9JmTPTudrD7QCcnTi6OuO0vV+9Kef2MMakRa0C5Pd6CRcQH3AN8AGgA1orIclXdGJRtCXBAVeeIyFXAXcCVIjIf73bAC4BpwLMicqLbJlKZPwIWq+omEfkC8G94U++bUfbmnjZ6+gc4u3rikbSqCQVkCexsiR5QjDHHrliHDf8F7wr5HLe8Fnh1mM3OBupUdbuq9gJLGTrUeDHwsFt+HLhIRMSlL3W3GN4B1LnyopWpeDf+AigB9sZybCb51ta3AlATFFDysn1MKclnZ2tHuqpljEmxWEd5fRbvC/8nLqkSeGqYzSqB3UGvG1xa2Dyq2g+0AWVRto1W5meAFSLSAHwSGNS8FnQsN4hIrYjUNjU1DXMIJhG19QeYVV5IRXHeoPTpE8ax50AXNrbDmLEp1k75G4F3AocAVHUrMClVlUrQV4APqWoV8DMG32HyCFW9T1VrVLWmoqJiVCt4vNi07xALpo0fkj55fD49/QMc6k5sKpb2nn4eeame379hJ5/GZKJYA0qPa2ICwF3cONzPzD3A9KDXVS4tbB5XZgle53ykbcOmu6lgFqrqapf+KN6V/WaUHe7uY8/BLk6aMrSfJHDG0nS4Z8i64bR19XHPn+vYtP8wL21v4XC3TeViTKaJNaD8RUT+FSgQkQ8AjwG/HWabtcBcEZklIrl4nezLQ/IsB65zy5cDK921LsuBq0QkT0RmAXOBNVHKPACUBHXcfwDYFOOxmSR66+3DAJw0ZegZytGAEv80cK/uOkBbVx8fr6liQOG1XQdHVE9jTPLFOsrrFrwRWW8CnwNWAPdH20BV+0XkJuAZvCG+D6rqBhG5A6hV1eXAA8AjIlIHtOIFCFy+ZcBGoB+4UVX9AOHKdOmfBZ4QkQG8APPpGI/NJNHm/V5AmRfmDKU4L5u87Cya2uM/Q9m49xDTJxRw2vQJrN7RSu3OVlQVbwyHMSYTxBRQVHVARJ4CnlLVmHuyVXUFXvAJTrstaLkbuCLCtncCd8ZSpkt/Engy1rqZ1Niy/zBFedlUTSgYsk5EmFScF3eT18HOXvYc7OLiBd61tTUzJ/LEqw28vvsgp8+YkJR6G2NGLmqTl3huF5FmYAuwxd2t8bZo25nj1+b9h5k3pTjimUNFAgFl475DACyY6jWjza4oBGD9HrstjzGZZLg+lK/gje46S1UnqupE4BzgnSLylZTXzhxTVJUtLqBEUlGUx6Hufrr7Yp+CZeO+Q0wqzqPc9cGUFOSQl53F1kabbNKYTDJcQPkkcLW7uBAAVd0OXAv8QyorZo49+w9109bVF3aEV0BFcT4Q+0iv/oEBdrV0MndS0ZG0QNPZ1rctoBiTSYYLKDmq2hya6PpRclJTJXOsest9wUebWuXISK8YO+b3Huymf0CZWVY4KH3S+Hy2Nh5OsKbGmFQYLqD0JrjOHIe2uSao4LOJUBMLc/GJxHyGsrPFm6plZtm4QemTivNobu+ltcM+hsZkiuFGeS0UkUNh0gXIT0F9zDFsW1M7peNymFiYGzGPL0uYWJQbR0DpZGJhLsX5g0+IJ4/3Pn5b3z7MOSeUJV5pY0zSRD1DUVWfqo4P8yhWVWvyMoPUNbYzu6Jo2GtDKopiG+mlquxs7WTmxHFD1k1yTWfWMW9M5oj1SnljhrWtqYM5FZGbuwImFefR0tFDn38gar7Wjl46evqZUTY0oJQU5FCUl83Wt60fxZhMEeuV8sYMEnojrK5eP83tPcyeVBhhi6MqivMYUK85a06U/pbALYNnThxapogwZ1LRkYEAxpj0szMUkxSB+blmx3CGEhjpVTdMc9XO1g7yc7KYND4v7PpZ5YXsarX71BuTKSygmKQIDAOOdsYRUFHkBYhtTcMElJZOZkwcR1aEPplppfnsP9SNf8Dur2JMJrCAYpKi6XAPviyhasLQ/o5QeTk+xudnRw0oXb1+Gg/3MCNMc1fAtNIC/ANKYwKzFxtjks/6UExSNB3uobwoF19WbLP/TirOP3LdSji7WsNffxKsstSbgHLPgS6mlgydjDJYaJ9PwDXnzBiuqsaYGNkZikmKpvZeyovC93WEU16cR11jOwMRmqt2tnSSJd5tgyM5ElAOdsVXWWNMSlhAMSPmH1AOdMQXUKaV5NPR62dnhE71na2dTC0pIDc78kd0mgsoew9ak5cxmcACihmxtq4+/KqURblCPlSlu1/KGw0Hh6zr8w+wu7WT6ijNXQCFedmUjsthz0Eb6WVMJkhpQBGRRSKyRUTqROSWMOvzRORRt361iFQHrbvVpW8RkYuHK9Pdu+VOEXlLRDaJyBdTeWzmqGY3wqssjjOUScX55GVnsW730Hua1Ld00D+gMY0Ym1ZSYGcoxmSIlHXKi4gPuAfv/u4NwFoRWa6qG4OyLQEOqOocEbkKuAu4UkTm490OeAEwDXg26H7xkcq8HpgOnOTuMDkpVcdmBms5ElBiP0PxZQkLpo3nzT0Hh6yra2zHJ8Ks8uEDSuWEAna12BmKMZkglWcoZwN1qrpdVXuBpcDikDyLgYfd8uPAReJNBLUYWKqqPe5eLHWuvGhlfh64Q1UHAFS1MYXHZoK0dPSSm51FcV58v09OrSpl/Z5D9IdMwbKtsZ0ZZeOi9p8EVJYWsNc65Y3JCKkMKJXA7qDXDS4tbB5V7QfagLIo20Yrczbe2U2tiPxBROaGq5SI3ODy1DY1NSV0YGawlvZeygpzh50UMtTC6SV09fmpC7oepb2nn71t3TE1d4EXUA739HOouy+ufRtjkm8sdcrnAd2qWgP8FHgwXCZVvU9Va1S1pqKiYlQrOFY1t/fE1X8S8I7KUgDeCOpHqXM3zYplkkk4OtJrzwE7SzEm3VIZUPbg9WkEVLm0sHlEJBsoAVqibButzAbg1275SeDUER+BGZZ/QDnQ2Ut5HCO8Ak4oL6S8KI9nNuw/krZmRysTxuUcGQU2nGml3n1RrNnLmPRLZUBZC8wVkVkikovXyb48JM9y4Dq3fDmwUlXVpV/lRoHNAuYCa4Yp8yngQrf8XuCt1ByWCXaws5cBjW+EV0BWlnDN2dNZuaWRXS2dbNx7iPqWTs49oSzi/F2hAoHHLm40Jv1SNspLVftF5CbgGcAHPKiqG0TkDqBWVZcDDwCPiEgd0IoXIHD5lgEbgX7gRlX1A4Qr0+3y+8AvROQrQDvwmVQdmzmqud27BW95HCO8gl1zzkzuXbWNB17YzoHOPnJ8wpkzJ8S8fXlhHrm+LAsoxmSAlM7lpaorgBUhabcFLXcDV0TY9k7gzljKdOkHgQ+PrMYmXi0d8V+DEmxKST4XnzKFh1/aCcB5J5QxLjf2j2VWljCtNN+uRTEmA9jkkGZEmtt7ycvOojDXl3AZ/3LxSZw4qZjTZpQmdE3JtNIC9hywa1GMSbexNMrLpEFLew9lRfEPGQ42o2wcX3r/XN57YkXMsxUHm1ZqV8sbkwksoJgRaenopawwseauZKksLeDtw9309ke/R70xJrWsycskrH9ggAMdvSysKklrPSpLC1CFtw91M33i8Df4ChbuPil2jxRjEmNnKCZhBzr6UBLvkE8WGzpsTGawgGISFpgUMpGLGpPJrpY3JjNYQDEJa+nwrkFJ9xnK1BK7Wt6YTGABxSSsub2H/Jwsxo1gyHAy5Of4KC/KY29b7AGlzz/A9qZ2vIkZjDHJYJ3yJmEt7ra/IxkynCyVpfk0xNHk9fT6/by0vYUTJxfxsTOqKM7PSWHtjDk+2BmKSVhLe09ct/1NpWlx3Belub2H1TtamD6hgO1NHfzujX0prp0xxwcLKCYh/f4BDnb2pb3/JKCytIA9B7tiasL644b9ZGdlce25Mzlj5gS27D9Mn9+uYTFmpCygmIS0dvR6Q4Yz6Aylu2+AA53Rb7TV1etnw95DnHvCRIrzc5g/dTy9/gG2Bd3kyxiTGAsoJiGBEV7lmXKG4q5FGa7Za1tTOwrMmzIe8O7JkpedxaZ9h1JdRWPGPAsoJiHN7YFZhjPjDKXSXYsyXMd8XVM7udlZzHBX1Gf7sjhxcjEb9x1mwEZ8GTMiFlBMQlraeynI8cU11XwqBS5uHO4Mpa6xnRPKCwdNQnnSlGI6evrZ32YTTBozEhZQTEKaO3oSvqlWKkwYl0NBji/q9CutHb20dvQyZ9Lg+9UH5v+yCyONGZmUBhQRWSQiW0SkTkRuCbM+T0QedetXi0h10LpbXfoWEbk4jjL/W0SshzXFWtp7M2aEF4BI4EZbkYPCtkbvYxEaUCYW5pKXnRXXhZHGmKFSFlBExAfcA1wCzAeuFpH5IdmWAAdUdQ5wN3CX23Y+3u2AFwCLgHtFxDdcmSJSA8R+/1iTkO4+P21dfRnTfxJQOWFc1IBS39JBYV42FSGBMEuEqSV210djRiqVZyhnA3Wqul1Ve4GlwOKQPIuBh93y48BF4l12vRhYqqo9qroDqHPlRSzTBZsfAF9L4TEZYFerd3fE8jTfByVUZWl+1CavXa2dzJw4LuyV/VNLC9jX1mUd88aMQCoDSiWwO+h1g0sLm0dV+4E2oCzKttHKvAlYrqpRL3sWkRtEpFZEapuamuI6IOPZ0dwBZM4Ir4BpJQU0t/fS3ecfsq69p5+Wjt4jo7tCVZYU0OfXI6PXjDHxGxOd8iIyDbgC+J/h8qrqfapao6o1FRUVqa/cGFQfCCiZdoYS5VqU3e6sKlJAmVoamLHYmr2MSVQqA8oeYHrQ6yqXFjaPiGQDJUBLlG0jpZ8OzAHqRKQeGCcidck6EDNYfUsH43J9FKR5luFQR4cODw0KO1s68YkcCTqhJhXnk50l7LORXsYkLJUBZS0wV0RmiUguXif78pA8y4Hr3PLlwEr1JmNaDlzlRoHNAuYCayKVqaq/V9UpqlqtqtVAp+voNymwo7kjY66QDxa4uDHQxxNsV2sH00rzyfGF/8j7soTJ4/PZZ9eiGJOwlF2Vpqr9InIT8AzgAx5U1Q0icgdQq6rLgQeAR9zZRCtegMDlWwZsBPqBG1XVDxCuzFQdgwmvvrnzyE2tMkllaQHF+dms39s2KL2n30/DgS7OmTUx6vaTivOSNqeX3aveHI9Sepmzqq4AVoSk3Ra03I3X9xFu2zuBO2MpM0yeomjrTeK6ev3sP9TNKZUl6a7KEFlZwsKqUt5oODgo/dWdB+kfUE6oiP6xmFScx2u7D3K4u8/uj2JMAsZEp7wZPfUtXod8Jl0lH+zUqhI27zs8aKTXi9uayRKYVV4YdduKYu+sq67Rros1JhEWUExcjozwysA+FIBTq0rpH1A2Bs0e/EJdM5WlBeTnRB9EMGm8d0wWUIxJjAUUE5cdgTOUDLkPSqiF072muDd2HwTgUHcf63YfHDLdSjgTxuWSnSUWUIxJkAUUE5d6N8Irb5hf++kyZXw+FcV5vNHgdcy/vK2FAYXZMQQUX5ZQXpRnAcWYBFlAMXGpb+6kuiz8xYGZQMTrmK/deQD/gLKsdjdFednMmBBbnSuK89hqAcWYhFhAMXHZ0dJB9TCd2+n2d6dPY1drJ5/9eS3PbmrkxgvnkB3h+pNQk4rz2H2gM+z0LcaY6CygmJgd6u6j6XAPs4cZfptuHzl1Gn9/RiUrNzcys2wcn35XdczbThqfjyp2j3ljEpAZt9szx4TtTV6H/OyKQprbe9Ncm+i+vfgUBgaUa86ZSV527P09k4qPjvRaMC3zrrUxJpNZQDExC9ygavakIprbW9Ncm+gK87L54VWnx71dWVEuPhvpZUxCrMnLxGxbUzvZWRJxxt6xIDsri5ll49j6tgUUY+JlAcXEbFtTOzPLxkWcYHGsmFNRxNbGw+muhjHHnLH9zWCSaltTx7DzYY0FcycXsbOlk97+gXRXxZhjigUUE5N+/wA7WzoyfoRXMsydVEz/gLLTzQpgjImNBRQTk90HuujzK7MrMvsalGQITNNiHfPGxMdGeZmYBI/wGutmVxQhAlsb27kkTXUIdz8VsHuqmMxmZygmJm+5TurjocmrINdHZWmBnaEYE6eUBhQRWSQiW0SkTkRuCbM+T0QedetXi0h10LpbXfoWEbl4uDJF5Bcufb2IPCgidoekJNq07zCVpQWUFBwff9Z5k4vZvP/Q8BmNMUekLKCIiA+4B7gEmA9cLSLzQ7ItAQ64+7/fDdzltp2PdzvgBcAi4F4R8Q1T5i+Ak4B3AAXAZ1J1bMejTfsOcfLU4nRXY9TMnzaebU0dNqeXMXFI5RnK2UCdqm5X1V5gKbA4JM9i4GG3/DhwkYiIS1+qqj2qugOoc+VFLFNVV6gDrAGqUnhsx5XuPj/bm9o5eer4dFdl1MyfOh7/gLJlv12PYkysUtkpXwnsDnrdAJwTKY+q9otIG1Dm0l8O2bbSLUct0zV1fRL4UrhKicgNwA0AM2ZYB2cs3nr7MAPqfckOJ1Jn8rEmMI/Xxn2HWDi9NO7tu3r9FORm5j1jjEmVsTjK617geVX9a7iVqnofcB9ATU2NjmbFjlWb3O10j6czlOkTCyjOy2bD3ra4tuv3D/CVZev47bq9TBiXwyWnTOWUSptk0hwfUtnktQeYHvS6yqWFzSMi2UAJ0BJl26hlisg3gQrg5qQcgQG8DvnCXN+YnsMrlIhw8rTxbNwbe8e8qvLVx9/gt+v2clb1BPKyfTz+agOtHZk9M7MxyZLKgLIWmCsis0QkF6+TfXlInuXAdW75cmCl6wNZDlzlRoHNAubi9YtELFNEPgNcDFytqjZnRhKt2tJEWVEeS9fu5perd42ZZq3hLJg2nk37DuMfiO1E9sVtLTz52h6++L45XHZ6FZ88byYCPP5KAwNqJ8Nm7EtZQFHVfuAm4BlgE7BMVTeIyB0icqnL9gBQJiJ1eGcVt7htNwDLgI3A08CNquqPVKYr68fAZOAlEXldRG5L1bEdTwYGlP2HuphSkp/uqoy6+VPH09XnZ0fz8FOwqCp3/+ktpozP5wsXzgFgwrhcPnTKVOpbOtj6tnXum7EvpX0oqroCWBGSdlvQcjdwRYRt7wTujKVMlz4W+4PSbsvbh+nuG2DmcdTcFXBqVSkAr+06cGQ6lkheqGumducBvr14Afk5Rzvjz5g5gec2v80Ldc2prKoxGcGulDdR1dZ7N9KaWTb25/AKNXdSEWWFuby0vWXYvD/96w4mj8/j42dNH5TuyxLOn13OtqaOuPpjjDkWWUAxUa2pP8D4/GwmjDs+rpAPlpUlnHtCGS9ta0Gj9IHsaO7g+beauObs8LcbPqt6Irm+LB78245UVteYtLOAYiJSVdbuaGVmWSHe9abHn/Nml7GvrZv6ls6Ief7fyzvJzhKuPnt62PUFuT4WTi/ld2/spa2rL1VVNSbtLKCYiBoOdLH/UDfV5cdfc1fA+bPLAHhpW/hmr87efh6r3c2iU6YwaXzkgQtnz5pId98AT77akFA9/ANK8+Ee+vw2gNFkLgsoJqK1rv+kuuz465APmFVeyOTxeby4LXyn+mO1DRzq7udT76yOWk5laQELq0r4xepdUZvPQg2o8vs39nL7bzfwn8++xcV3P8+fNzfGcwjGjBoLKCai5zY1Ul6Uy+Qov7zHOhHhghMnsXJzI4e6BzdX9fsHuP+F7Zw5cwJnzpw4bFmfOGcmWxvbeXl7a0z79g8ov1i9i79ta+HUyhI+unAaIvDph9fy9Pr9CR2PMalkASUNAhcHBj8yTVevn5WbG7l4wRSyjtP+k4Brz51JZ6+fJ14Z3Fz19Ib97G7t4rPvPiGmci49bRplhbnc/9ftMeX/48b9bNp3iA+/YypX1EznvBPK+N0/vZuFVaV8+dHXeKPhYLyHYkxKWUAxYa3a0khXn58Pv2NququSdu+oKuH0GaU88tJOBtxV84e7+/ju7zcxd1IRH5g/OaZy8nN8/MN51Ty3uZG6xugXOm7ef4i/bm3m7FkTeeec8iPpBbk+7r+uhrLCPL609HW6em16fZM5LKCYsH7/5j7KCnM5e9bwTTmZJhVngNefX8325g4eerGegQHl27/byP5D3dx1+an4smI/g7v23BnkZWdx75+3Rcyz92AXj7/SwNSS/LABvbwojx9ccSo7mju46+nNCR2PMalgAcUM0dbZx8rNjXxwwRSyffYRAfjQO6by/pMnccfvNvLOu1ayrLaBG94zmzNmTIirnLKiPK5/ZzW/fm0Pa3YM7Uvp8w/wxV+9Rv+AcvVZM8iJ8Pc/f3Y5159fzUMv1kccMGDMaLNvCzPEz17cQWevn0+eOzPdVckYOb4sfnztmXzqndVUTSjg7isX8tWL5yVU1pcumktlaQFff/LNQXeEHBhQvvrYOmp3HuCy0yopL86LWs6/LDqJWeWFfPWxNzjcbde3mPSzgGIGOdzdx4Mv7OAD8yczf9rxc/+TWGT7svjmRxfw2D+ez2WnV8XV1BVsXG4237nsFLY2tnPt/avZ19ZF4+Fuvvzo6zz1+l6+evG8mG7qVZDr4z+uWMi+ti7+7an1cQ1HNiYVbELFUeAfUP6wfh+/fnUPDQc6ae/uZ96UYmpmTmRCYW66qzfIvau2cai7ny++b266qzKmXThvEvdccwZfWfY6531vJQA5PuEr7z+RL1wwm1+t2T1MCZ4zZ07gy+8/kf/801ssrCrl0++alcpqGxOVBZQU29nSwc3L1vHKzgNMLcnntOmlbN5/mFVbmvjr1mbed9Ik3j23It3VBOCvW5v48V+28fGaKt5RZXcZTLUPnzqVOZOKeKGumbbOXv7+jKqEZiW46cI5vLmnjTtXbGJCYQ6XnV6VgtoaMzwLKCmiqiyr3c0dv91IVpZw95ULuXRhJb4s4Zerd3Gws5cVb+7jjxvfZtO+Q1wwr4LpaZwifsPeNv7xkVeoKMpj/tSSjLw2Jt3C/U2uOWfGiMqcN6WYeVOKR1RGVpbwwytP4zMP13LzsnXsPdjN595zgg2oMKPOAkoKNLf3cMsTb/Lsprc5f3YZ/3HFQqaVFgzKUzoul2vOmckbDQd56vU9fOi//sp3LjuFxadVjmpdVZU/rN/PVx9bR7Yvi2vPmUlutn0RxSqewBsp70iDEkBhXjY/+9RZ/POydfzgmS38dt1ePnneTN57YgXTSgrIyhJUlYf+Vk9nn5/OXj9dvX46e/vp7PUzb0oxqoovKwtfFviyssj1CYV52RTmZVPkHoV52YzPz2ZiYa4FLDNESgOKiCwC/gvwAfer6vdD1ucBPwfOxLuX/JWqWu/W3QosAfzAF1X1mWhlulsFLwXKgFeAT6rqqN7Mu7d/gF+t2cUPn32Ljl4///bhk/n0O2eRFaXz9tSqUqZPGMdzm9/mS0tf5zev7+XWS05i7uSR/Wodjqry4rYWfvyXbfx1azOnVI7nw++YRknB8TdN/ViRn+Pjf685nY9umMb//eMWvv7kesDrm8nL9tHT76fPH6Hjfl18+xJgXK6P4vwcivKzObWyhPLiPEoKcigdl+M9F+RSkOtDBLJEENyz+3cILIuA4JbBrZcj2xXk+CgpyCE/J+u4nfX6WJGygCIiPuAe4ANAA7BWRJar6sagbEuAA6o6R0SuAu4CrhSR+Xj3i18ATAOeFZET3TaRyrwLuFtVl4rIj13ZP0rV8akqh3v6aTzUw7amdl7a1sLydXtp7ejl/Nll3H7pAk6MMShMKMxl2efO44EXdvC/K+v4wN3Pc+4JE3n/yZM5bXopU0ryqSjOC3uvjWADA0qvf4A+/wC9/QP0+ZXe/gF6/QN09/lpau+hvrmDDXsP8cLWZvYf6mbCuBy+8ZH5XHfeTJbVJjYTbrocj81ysR7z9edXc9qMUtbtbmNXayd9/gFys7Oob+6gIMfHuFwfBbnZjMv1lvNzvC9+Ve9zNKDQP+B9jnr6B+ju99Pb5y139flp7+mnvbufwz39tHf3saa+leb2Hrr7Ujcbsk+E/FwfBTlZVE0YR0lBDuMLcigpyGZ8vhfESgpyKMj1/k/EBbFAwOLI8tGglZst5Gf7yMvxkZedRX6Oj/ycwLOP/OysIWdiqt7/WVevny53ttfR009bVx+/f2MfXX1+ut067+Hd8TTPlVucl+3qPfQxviCHbJ/gE8GXJWQdeeaYCKaSqqGGInIecLuqXuxe3wqgqt8LyvOMy/OSiGQD+4EKjt5b/nvB+dxmQ8oEvg80AVNUtT9035HU1NRobW1t3Mf23RWb+PlL9YP+eXKzs3jfvEl84twZvGtOedQ3P1pbfEt7D0vX7uaJVxrYHnIv86K8bLLEazPPEu9D1j+gLngMRP71GWLCuBzOn13O++dP4pJTph65Ze3x+AVtkqsv5Iu2zz+A9xWjqILC0dduOfCpDXwXudVuvdLnV++LuddPt/uSDjwH9tXd52cgxaOmg/+lY/na9GV5Z1cFOT7GF2TT3ef9sDvc009vf/yB92gwHBwog8/ysuTomV8gIIn7rjj62lu+62Oncu4JZXHXw6uLvKKqNaHpqWzyqgSCxz42AOdEyuMCQRtek1Ul8HLItoHOhXBllgEHVbU/TP5BROQG4Ab3sl1EtsRxTFFtBX4SW9ZyYNDlzZ9IViVisBN4Hbg3cpYh9ctAVsfksDqOXKbXD8LU8byvjai8sFc9H3ed8qp6H3BfOusgIrXhonumyPT6gdUxWayOI5fp9YPRq2Mqh2nsAYLviVrl0sLmcU1eJXid85G2jZTeApS6MiLtyxhjTAqlMqCsBeaKyCwRycXrZF8ekmc5cJ1bvhxYqV5D6nLgKhHJc6O35gJrIpXptvmzKwNX5m9SeGzGGGNCpKzJy/WJ3AQ8gzfE90FV3SAidwC1qroceAB4RETqgFa8AIHLtwzYCPQDN6qqHyBcmW6X/wIsFZHvAK+5sjNVWpvcYpDp9QOrY7JYHUcu0+sHo1THlI3yMsYYc3yxS12NMcYkhQUUY4wxSWEBZRSJyCIR2SIidSJyyyjv+0ERaRSR9UFpE0XkTyKy1T1PcOkiIv/t6vmGiJwRtM11Lv9WEbku3L4SrN90EfmziGwUkQ0i8qUMrGO+iKwRkXWujt9y6bNEZLWry6NuwAhuUMmjLn21iFQHlXWrS98iIlEvwE2wrj4ReU1EfpeJdRSRehF5U0ReF5Fal5Yx77Uru1REHheRzSKySUTOy5Q6isg897cLPA6JyJfTXj9VtccoPPAGEWwDTgBy8WZPmj+K+38PcAawPijt34Fb3PItwF1u+UPAH/AuzD0XWO3SJwLb3fMEtzwhSfWbCpzhlouBt4D5GVZHAYrccg6w2u17GXCVS/8x8Hm3/AXgx275KuBRtzzfvf95wCz3ufAl+f2+Gfgl8Dv3OqPqCNQD5SFpGfNeu/IfBj7jlnOB0kyro9uHD2+WkZnprl/SDsoew77p5wHPBL2+Fbh1lOtQzeCAsgWY6panAlvc8k+Aq0PzAVcDPwlKH5QvyXX9Dd6cbRlZR2Ac8CreTA3NQHbo+4w3GvE8t5zt8knoex+cL0l1qwKeA94H/M7tM9PqWM/QgJIx7zXeNXE7cAOXMrGOQWV+EPhbJtTPmrxGT7ipaEZ3rvqhJqvqPre8H5jsliPVdVSOwTW7nI53BpBRdXRNSa8DjcCf8H65H9Tw0/4MmloICJ5aKJV/xx8CXwMCE0ZFm5ooXXVU4I8i8op40yFBZr3Xs/DmB/yZazq8X0QKM6yOAVcBv3LLaa2fBRQDgHo/T9I+hlxEioAngC+r6qHgdZlQR1X1q+ppeGcBZwMnpbM+oUTkI0Cjqr6S7roM412qegZwCXCjiLwneGUGvNfZeE3EP1LV04EO3KS1ARlQR1xf2KXAY6Hr0lE/CyijJ5apaEbb2yIyFcA9N7r0eKe+SQoRycELJr9Q1V9nYh0DVPUg3uwM5xF52p94pxZKhncCl4pIPd79gd6Hd/+gTKojqrrHPTcCT+IF50x6rxuABlVd7V4/jhdgMqmO4AXkV1X1bfc6rfWzgDJ6YpmKZrQFT30TPF3NcuAf3MiQc4E2dxr9DPBBEZngRo980KWNmIgI3uwGm1T1PzO0jhUiUuqWC/D6eDYRedqfeKcWGjFVvVVVq1S1Gu8ztlJVP5FJdRSRQhEpDizjvUfryaD3WlX3A7tFZJ5Lughv5o6MqaNzNUebuwL1SF/9ktk5ZI9hO88+hDd6aRvw9VHe96+AfUAf3q+vJXht5c/hzbz/LDDR5RW8G5ltA94EaoLK+TRQ5x6fSmL93oV3ev4G3uz6r7u/VybV8VS8aX3ewPsCvM2ln4D3ZVuH1/SQ59Lz3es6t/6EoLK+7uq+BbgkRe/5BRwd5ZUxdXR1WeceGwL/C5n0XruyTwNq3fv9FN4oqIypI1CIdzZZEpSW1vrZ1CvGGGOSwpq8jDHGJIUFFGOMMUlhAcUYY0xSWEAxxhiTFBZQjDHGJIUFFGOMMUlhAcWYYYjIFBFZKiLb3NxTK0TkxBGWGTx9++sicn4CZVTL4NsRnC4icd/6WkQ+It6tuY0ZEQsoxkThruB/ElilqrNV9Uy8mXgnR9/yyD0oov2PXaiqp7nHi0mo7r8C/53Adr8HPioi45JQB3Mcs4BiTHQXAn2q+uNAgqquA14TkedE5FV3prEYjpw1bBGRn+NdTT89fLFDicgqEalxy+VuPq7ADMc/EJG14t0c6XNhti0GTnV1Q0SKRORnrm5viMjHXPoiV+d1IvKcOx4FVgEfSeDvY8wR2cNnMea4dgoQbubebuAyVT0kIuXAyyISmJttLnCdqr48TNl/FhE/0KOq50TJtwRv7qWzRCQP+JuI/JHBM8nW4AWwgG+4bd4B4OZqqgB+CrxHVXeIyMSg/LXAu/FuxGVMQiygGJMYAb4r3rTrA3j3kAg0g+2MIZiA1+TVHEO+DwKnikhgcscSvKD1VlCeqXj37wh4P97kkACo6gER+SjwvKrucGmtQfkbgWkx1MWYiCygGBPdBo7O0hvsE0AFcKaq9rnmqXy3riPBffVztBk6PyhdgH9S1UGzwErQ/d+BrpBt4pXvyjAmYdaHYkx0K4E8OXpXQUTkVLz7dze6YHKhez1S9cCZbjk4iD0DfF68+8UgIie6ad+DbQLmBL3+E3BjUJ0nAC8D73HT0RPS5HUig5vMjImbBRRjonAd1pcB73fDhjcA3wNWADUi8ibwD8DmJOzuP/ACx2tAeVD6/Xj34njVDRP+CSGtC6q6GSgJ3GcE+A4wQUTWi8g6vOa1JuAG4Ncu7dGgIi7EG+1lTMJs+npjxggR+QpwWFXvj3O7ycAvVfWi1NTMHC/sDMWYseNHQE8C280A/jnJdTHHITtDMSaFRGQ1kBeS/ElVfTMd9TEmlSygGGOMSQpr8jLGGJMUFlCMMcYkhQUUY4wxSWEBxRhjTFL8f/OdADCdgLRuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Car_data_target_seat_door_fuel['Car_Fuel(cc)'])\n",
    "plt.title('Distribution of Car Fuel(cc)', fontsize = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\env\\ad\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Car Mileage(km)')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Car_data_target_seat_door_fuel['Car_Mileage(km)'])\n",
    "plt.title('Distribution of Car Mileage(km)', fontsize = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.plot(kind=\"scatter\", x=\"Car_Seat\", y=\"Car_Price\")\n",
    "plt.title('Car Price for different Number of Seats', fontsize = 20) \n",
    "plt.ylabel('Car Price', fontsize = 15) \n",
    "plt.xlabel('Car Seat', fontsize = 15) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86261</td>\n",
       "      <td>3997</td>\n",
       "      <td>0</td>\n",
       "      <td>2005 Nissan Tiida Axis</td>\n",
       "      <td>2005</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Tiida</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>138150</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1500</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79395</td>\n",
       "      <td>7997</td>\n",
       "      <td>0</td>\n",
       "      <td>2009 Toyota Wish</td>\n",
       "      <td>2009</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Wish</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>100300</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1800</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85473</td>\n",
       "      <td>9940</td>\n",
       "      <td>1</td>\n",
       "      <td>2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...</td>\n",
       "      <td>2007</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>102425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88342</td>\n",
       "      <td>8940</td>\n",
       "      <td>1</td>\n",
       "      <td>2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...</td>\n",
       "      <td>2006</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>109425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>71719</td>\n",
       "      <td>9400</td>\n",
       "      <td>1</td>\n",
       "      <td>2010 Nissan X-Trail 20XT</td>\n",
       "      <td>2010</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>X-Trail</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>125384</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_No  Car_Price  Car_ORC  \\\n",
       "0  86261       3997        0   \n",
       "1  79395       7997        0   \n",
       "2  85473       9940        1   \n",
       "3  88342       8940        1   \n",
       "5  71719       9400        1   \n",
       "\n",
       "                                           Car_Title  Car_Year   Car_Brand  \\\n",
       "0                             2005 Nissan Tiida Axis      2005      Nissan   \n",
       "1                                   2009 Toyota Wish      2009      Toyota   \n",
       "2  2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...      2007  Mitsubishi   \n",
       "3  2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...      2006  Mitsubishi   \n",
       "5                           2010 Nissan X-Trail 20XT      2010      Nissan   \n",
       "\n",
       "   Car_Model Car_Location  Car_Mileage(km)       Car_Type   Car_Gear  \\\n",
       "0      Tiida      Dunedin           138150      Hatchback  Automatic   \n",
       "1       Wish      Dunedin           100300  Station Wagon  Automatic   \n",
       "2  OUTLANDER     Auckland           102425         RV/SUV  Automatic   \n",
       "3  OUTLANDER     Auckland           109425         RV/SUV  Automatic   \n",
       "5    X-Trail     Auckland           125384         RV/SUV  Automatic   \n",
       "\n",
       "   Car_Seat  Car_Fuel(cc)  Car_Door_Num Car_Fuel_Type Car_Color  \n",
       "0       5.0          1500           5.0        Petrol    Silver  \n",
       "1       7.0          1800           5.0        Petrol     White  \n",
       "2       5.0          2400           5.0        Petrol    Silver  \n",
       "3       5.0          2400           5.0        Petrol     Black  \n",
       "5       5.0          2000           5.0        Petrol     Black  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.plot(kind=\"scatter\", x=\"Car_Year\", y=\"Car_Price\")\n",
    "plt.title('Car Price for different year of car', fontsize = 20) \n",
    "plt.ylabel('Car Price', fontsize = 15) \n",
    "plt.xlabel('Car Year', fontsize = 15) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Delete outlier: Car year earlier than 1985. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target_seat_door_fuel.drop(Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Year'] < 1990)].index, axis=0, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.plot(kind=\"scatter\", x=\"Car_Year\", y=\"Car_Price\")\n",
    "plt.title('Car Price for different year of car', fontsize = 20) \n",
    "plt.ylabel('Car Price', fontsize = 15) \n",
    "plt.xlabel('Car Year', fontsize = 15) \n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#box plot Districts/Total Price\n",
    "f, ax = plt.subplots(figsize=(15, 10))\n",
    "fig = sns.boxplot(x='Car_Type', y=\"Car_Price\", data=Car_data_target_seat_door_fuel)\n",
    "plt.title('Car Price for Different Car Type', fontsize = 20) \n",
    "plt.ylabel('Car Price', fontsize = 15) \n",
    "plt.xlabel('Car Type', fontsize = 15) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x=\"Car_Brand\", y=\"Car_Price\", \n",
    "              data=Car_data_target_seat_door_fuel, jitter=True,\n",
    "              palette=\"Set2\",  # 设置调色盘\n",
    "              dodge=True,  # 是否拆分\n",
    "             )\n",
    "plt.title('Car Price for Different Car Brand', fontsize = 20) \n",
    "plt.ylabel('Car Price', fontsize = 15) \n",
    "plt.xlabel('Car Brand', fontsize = 15) \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10065 entries, 0 to 13438\n",
      "Data columns (total 16 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Car_No           10065 non-null  object \n",
      " 1   Car_Price        10065 non-null  int32  \n",
      " 2   Car_ORC          10065 non-null  int32  \n",
      " 3   Car_Title        10065 non-null  object \n",
      " 4   Car_Year         10065 non-null  int32  \n",
      " 5   Car_Brand        10065 non-null  object \n",
      " 6   Car_Model        10065 non-null  object \n",
      " 7   Car_Location     10065 non-null  object \n",
      " 8   Car_Mileage(km)  10065 non-null  int32  \n",
      " 9   Car_Type         10065 non-null  object \n",
      " 10  Car_Gear         10065 non-null  object \n",
      " 11  Car_Seat         10065 non-null  float64\n",
      " 12  Car_Fuel(cc)     10065 non-null  int32  \n",
      " 13  Car_Door_Num     10065 non-null  float64\n",
      " 14  Car_Fuel_Type    10065 non-null  object \n",
      " 15  Car_Color        10065 non-null  object \n",
      "dtypes: float64(2), int32(5), object(9)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only keep valuable columns \n",
    "\n",
    "Car_data_target_final = Car_data_target_seat_door_fuel.drop(['Car_No', 'Car_Title', 'Car_Model', 'Car_Color'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3997</td>\n",
       "      <td>0</td>\n",
       "      <td>2005</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>138150</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1500</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7997</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>100300</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1800</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9940</td>\n",
       "      <td>1</td>\n",
       "      <td>2007</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>102425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8940</td>\n",
       "      <td>1</td>\n",
       "      <td>2006</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>109425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9400</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>125384</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Car_Price  Car_ORC  Car_Year   Car_Brand Car_Location  Car_Mileage(km)  \\\n",
       "0       3997        0      2005      Nissan      Dunedin           138150   \n",
       "1       7997        0      2009      Toyota      Dunedin           100300   \n",
       "2       9940        1      2007  Mitsubishi     Auckland           102425   \n",
       "3       8940        1      2006  Mitsubishi     Auckland           109425   \n",
       "5       9400        1      2010      Nissan     Auckland           125384   \n",
       "\n",
       "        Car_Type   Car_Gear  Car_Seat  Car_Fuel(cc)  Car_Door_Num  \\\n",
       "0      Hatchback  Automatic       5.0          1500           5.0   \n",
       "1  Station Wagon  Automatic       7.0          1800           5.0   \n",
       "2         RV/SUV  Automatic       5.0          2400           5.0   \n",
       "3         RV/SUV  Automatic       5.0          2400           5.0   \n",
       "5         RV/SUV  Automatic       5.0          2000           5.0   \n",
       "\n",
       "  Car_Fuel_Type  \n",
       "0        Petrol  \n",
       "1        Petrol  \n",
       "2        Petrol  \n",
       "3        Petrol  \n",
       "5        Petrol  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical features are:  ['Car_Brand', 'Car_Location', 'Car_Type', 'Car_Gear', 'Car_Fuel_Type']\n",
      "Numerical features are:  ['Car_Price', 'Car_ORC', 'Car_Year', 'Car_Mileage(km)', 'Car_Seat', 'Car_Fuel(cc)', 'Car_Door_Num']\n"
     ]
    }
   ],
   "source": [
    "# distinguish features into categorical and numerical features \n",
    "\n",
    "categorical_features = Car_data_target_final.select_dtypes(include = [\"object\"]).columns\n",
    "numerical_features = Car_data_target_final.select_dtypes(exclude = [\"object\"]).columns\n",
    "print('Categorical features are: ', categorical_features.tolist())\n",
    "print('Numerical features are: ',numerical_features.tolist())\n",
    "\n",
    "Car_data_categorical_columns = Car_data_target_final[categorical_features]\n",
    "\n",
    "Car_data_numerical_columns = Car_data_target_final[numerical_features].drop(['Car_Price'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dummy values \n",
    "\n",
    "df_categorical_columns_dummies = pd.get_dummies(Car_data_categorical_columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Brand_Aston</th>\n",
       "      <th>Car_Brand_Audi</th>\n",
       "      <th>Car_Brand_BMW</th>\n",
       "      <th>Car_Brand_Bentley</th>\n",
       "      <th>Car_Brand_Cadillac</th>\n",
       "      <th>Car_Brand_Chevrolet</th>\n",
       "      <th>Car_Brand_Chrysler</th>\n",
       "      <th>Car_Brand_Citroen</th>\n",
       "      <th>Car_Brand_Daihatsu</th>\n",
       "      <th>Car_Brand_Fiat</th>\n",
       "      <th>...</th>\n",
       "      <th>Car_Type_Ute</th>\n",
       "      <th>Car_Type_Van</th>\n",
       "      <th>Car_Gear_Automatic</th>\n",
       "      <th>Car_Gear_Manual</th>\n",
       "      <th>Car_Gear_Tiptronic</th>\n",
       "      <th>Car_Fuel_Type_Diesel</th>\n",
       "      <th>Car_Fuel_Type_Electric</th>\n",
       "      <th>Car_Fuel_Type_Hybrid</th>\n",
       "      <th>Car_Fuel_Type_LPG</th>\n",
       "      <th>Car_Fuel_Type_Petrol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Car_Brand_Aston  Car_Brand_Audi  Car_Brand_BMW  Car_Brand_Bentley  \\\n",
       "0                0               0              0                  0   \n",
       "1                0               0              0                  0   \n",
       "2                0               0              0                  0   \n",
       "3                0               0              0                  0   \n",
       "5                0               0              0                  0   \n",
       "\n",
       "   Car_Brand_Cadillac  Car_Brand_Chevrolet  Car_Brand_Chrysler  \\\n",
       "0                   0                    0                   0   \n",
       "1                   0                    0                   0   \n",
       "2                   0                    0                   0   \n",
       "3                   0                    0                   0   \n",
       "5                   0                    0                   0   \n",
       "\n",
       "   Car_Brand_Citroen  Car_Brand_Daihatsu  Car_Brand_Fiat  ...  Car_Type_Ute  \\\n",
       "0                  0                   0               0  ...             0   \n",
       "1                  0                   0               0  ...             0   \n",
       "2                  0                   0               0  ...             0   \n",
       "3                  0                   0               0  ...             0   \n",
       "5                  0                   0               0  ...             0   \n",
       "\n",
       "   Car_Type_Van  Car_Gear_Automatic  Car_Gear_Manual  Car_Gear_Tiptronic  \\\n",
       "0             0                   1                0                   0   \n",
       "1             0                   1                0                   0   \n",
       "2             0                   1                0                   0   \n",
       "3             0                   1                0                   0   \n",
       "5             0                   1                0                   0   \n",
       "\n",
       "   Car_Fuel_Type_Diesel  Car_Fuel_Type_Electric  Car_Fuel_Type_Hybrid  \\\n",
       "0                     0                       0                     0   \n",
       "1                     0                       0                     0   \n",
       "2                     0                       0                     0   \n",
       "3                     0                       0                     0   \n",
       "5                     0                       0                     0   \n",
       "\n",
       "   Car_Fuel_Type_LPG  Car_Fuel_Type_Petrol  \n",
       "0                  0                     1  \n",
       "1                  0                     1  \n",
       "2                  0                     1  \n",
       "3                  0                     1  \n",
       "5                  0                     1  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_categorical_columns_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train : (7045, 70)\n",
      "X_test : (3020, 70)\n",
      "y_train : (7045,)\n",
      "y_test : (3020,)\n"
     ]
    }
   ],
   "source": [
    "# Create dataset for ML \n",
    "# Split dataset for the training and testing \n",
    "car_train_len = int(len(Car_data_target_final)*0.7) \n",
    "\n",
    "y_train = Car_data_target_final['Car_Price'][:car_train_len]\n",
    "\n",
    "y_test = Car_data_target_final['Car_Price'][car_train_len:]\n",
    "\n",
    "\n",
    "df_train_num = Car_data_numerical_columns[:car_train_len] \n",
    "df_train_dum = df_categorical_columns_dummies[:car_train_len]\n",
    "X_train = pd.concat([df_train_num, df_train_dum], axis = 1)\n",
    "df_train = pd.concat([X_train, y_train], axis = 1)\n",
    "\n",
    "\n",
    "df_test_num = Car_data_numerical_columns[car_train_len:] \n",
    "df_test_dum = df_categorical_columns_dummies[car_train_len:]\n",
    "X_test = pd.concat([df_test_num, df_test_dum], axis = 1)\n",
    "df_test = pd.concat([X_test, y_test], axis = 1)\n",
    "\n",
    "\n",
    "print(\"X_train : \" + str(X_train.shape))\n",
    "print(\"X_test : \" + str(X_test.shape))\n",
    "print(\"y_train : \" + str(y_train.shape))\n",
    "print(\"y_test : \" + str(y_test.shape))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Price                1.000000\n",
       "Car_Fuel(cc)             0.417299\n",
       "Car_Year                 0.381062\n",
       "Car_Fuel_Type_Diesel     0.367488\n",
       "Car_Brand_Lamborghini    0.363655\n",
       "                           ...   \n",
       "Car_Brand_Smart          0.001320\n",
       "Car_Brand_Cadillac       0.001038\n",
       "Car_Type_Sedan           0.001023\n",
       "Car_Brand_Renault             NaN\n",
       "Car_Brand_Rolls-Royce         NaN\n",
       "Name: Car_Price, Length: 71, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZLq/HmyTLRAYGu6/5e1d2L7uXZkvpjX3NNsuu09fpza359Gg1vOnUt+UFDbzZOp/qy8a5VGklIhXzx0919WaCAwsmZPvHL8nCr71J1qdf8/hrWzxucmGZgdApk6ykbuBnBI/6x4FbJF1kZvW8amb2v678Z4DtXRX136xOkZ7qO8Oy0H1dCXih7HxJv4gycndJ+nrtpDhz/Lrrw2Zx/yqS/h7Ln0x7wQP1vkTmA/co6tESPGbPdcevJwyQxL8/yr2/ro22E4nEEEZdXZVeFdgFeMDMHjKzxcDZQDMr20HAWR24hFLSgNkZBlv39U7garIZXNH5XzGznQjhIa+LIgc1no19+AVQS0fwNeCfZrYlYXa8TotrnBhNG/cS4jO/kTt+NnCgpFcQ4i+fdMeuIxsgNwDOI2jQEvdfX9Sg15L93Tl/KCqSSCSGGB30kn058G/3/vG4rw+S1gXWJwi+1JgQfz9ulPSOfl5OA8kkO7h0TPdV0obA5ZKuKjn/3VEcYAxB33UL4PZ47Pz4dwbwzri9e23bzC6W5GeMRXiT7KuA30rysZh/JQyiTwPn5M69HjgqKv08YmYLFZhMUBgqFIH3WrKPPPAvqxmkvEnN66T6be+RWOaR6r1hy7xn+2OeLfNWrSJW4Ps6z5njyupsVzAhf06VAHivK1vW3tLxxd6znaKq5/ILK2XPfWWfe9n+MrxYQVk93qN53rip9W1vXvVawvljvi5/rd4cXPb9KfO2fWn8tML93jN4wsJW//bt08b65GHAYW7XSfF/vj8cCPzBrMHte10ze0LSBoQkEHeY2YP9rB9IA2anGHTdVzN7UNLThIHwGX9+HIg+D+wcxc5Po1Hftbaw00MHPnMzuyE6CK3m9i2WNIMQV7kFsK87dr+kqcDbgBvi7hnABwkDaOaHn0gkhjVVvWT9A3EJT9C4XLV23FfEgThJzlj/E/HvQ3GisT0woAEzmWQ7w6DrvkpanWByKDLfrkQYQGcrqOe8uUKV1wDvjXW/GVi5efGGvmwGdAPP5Q79APhSLhtJjRuBw8kGzBuAI0jrl4nEiKKDXrK3ABtLWl/SOMKg2MfbNf4erUz224KklSWNj9urAq8mOB0OiDTD7ACDrPt6paQeYCxwpJk9rcZsIZjZLEm3xfr/TbVB6OvAWZLuIphMH2tRfqKkmXFbwCFm1pMtyYKZ3UX0ji3gOuAthPsA4cu9ASXrl3m8GbYh8NyZqXwZHzBexRTqKTPP5o8NNoNh2uwPVfoxZPq6nKKTvA5vFeEKqGYaLvvuVjGlVzHjPztlg4Zjxcnb2qNTWrJmtlTSpwlJILqBU8zsLknHAtPNrDZ4HgicHRPS19gc+JWkXsLE8HjvXdtf1NhGIjE0efjBB+pf1CoDpi9TtgZVheU5YI5ENthwwz77HnpwQFayBvqjfNRpOjlgLms22HDDAd+0f39y/0qDyit+/seh8ZTVBmmGmUgkEomOkbKVJAYVLUPd1xb9WAW4vODQG8wsv165TPFP6v7J3M8qy4LH/Sy03af6/IxyoB60iZHDiouyfwkvplGaYqwf5mI/K/VWk4GkKKuiJTwQ1J0GzMQgMlR0X+OguN3y7kcikRi+jOQZ5si9sjbRyNeCLbw+SXtI+ssg9OcYSZ9vXTKRSIwkOqklO9RIM0xGvhZsi+urWkd3Lii4o0gaY1Yi2pnDm1jLNEWrpNLqD50UOGjFUHBgqdqPIdPXBm3c4s+9yndjbG+mSVvmNOY9Vb2JtMHM6fV2mzj9lHq0uvN9G536TAbjs0ozzJHPSNeCLbw+M7s2vp0s6Q+S7pV0Zhxga21/R9KtwJHxb61fG9feSzpe0t2Sbpf0/XzjkjaU9Nc4s73WXcNpkn4Z13C/W+EeJhKJIc5InmGmATMw0rVgy66vxvYEEYEtCLGRr3bHnjOzHczsWwRhhO3i/g8Cp0ZHof2ALc1sG+Cb9OUk4DNmtmPs/8/dsbWB3czsc/mT5LRkzzr77CbdTyQSQ4WRPGAmk2xzRpIWbDNuNrPHAaI4wXrAP+Mxrwt7MvBBSZ8jZCTZBZhNeLD4TVwLbVgPVdCL3Q04z4kceBvXeWWmXi+dFeIwg4mpzIzkTVxeX3OwzIWDbZ4dMmIAI0y4oIqJvkrsbpmGa+P3rb128wy2KXUwhB7UPfTiSztFGjADI10Ltuz68vUXteGv84+Eme0VwIxaqImkXYA3xDY+DbzendMFvNgkL11b9zGRSAxt0hrmyGeka8EWXp+k11bsPgBmtpAgU/UL4NRYz2RgipldAvwvsG3unDnAw5IOiOUVHY4SicQIJJlkRzgjXQu2yfUdQUl+uSacSViz/Ht8vyLwJ0kTCCbsPmuRhJn6LyQdTbgPZwOz2mnUm6DKPGN9YLenTHezrJ7+mLsGwzw7ZDxPK9ynwe5rvg9lbZR5wC7Lvja05bx28yZZ39cJS+fXt70OcpXvfVnblfpXoc62GcEzzDRgRszsSeDdBYdeVXLKViX7fZ3rlex/JH++mR3aqg4zmw7sEbefA/Zu1Qd3btn13Q9c5cp9uqhtx2uAU2vrjmb2H8JaZr69Y9z2w8CbCsocWrH7iURimDBcZ49VSANmojKSLgA2pHGNMpFIJOpoOWWMWRakAbMDaJRowZrZfgOto79UMQt602unzFf9oVPm2SHjeToEvGSr1l9FDMCb7sf0ZNtlXq/t4j1Pm/W7zKvb4/vqv99VhBWW1+emigmkhyNpwOwASQs2kUgkAskkm0gkEolEFZJJNtEuktYETgB2Bl4EngaOMLOm2rQt6nwEmEuIlewGjjazPzU9aWBt7WRmz5Yc7wG8yfkd0Zmp3XbWA/5iZk2dqCYver6+7dMnLe0am9XlzLATF82ub8+ZuHphnd5L0Z9bluW+PwzEPFtmjuskVUzdvh9lwff+Xg5GX/M6rGVmz/HO23Sx+574fvvPd+nY4r6O78nqWdQ9qbCMv3feG7Yhwbn7fubNn75PL5txQX37yR2zlQ9/nROWZiHLPp3YKrMfrm8/N2X9+naZ2dZfm0+D1ynSDDPRFiNdzD2yoIkYQSntiKwnEolhyAgOKxm5V7Z8Geli7oVI2k7SjVGE/QJJK8f9V0k6QdJ04HBJO0qaJWkW8Kkm9dW1ZH977gVlxRKJxBBC3d2VXsORNMMcHFqJuc9RyF15o6SL4rGNgUMq6NNeGWeRG9AYV9lwvqSvmNnzkroJGrbbmFlNm/ZZM9tB0icJknwfIRNzP1bSW4EPt+jHxKg7C/Bw9KD9LUFk/WpJx8Y6j4hlxkVxeSTdDnzazK6R9L2yBvJasi/F/VWC05dOXK2+3e0mtN4c18m0X1Vo1zw7WGZYTxUvySr9GOx7WbV+b6qsojlcWk+JGdbj6/epxPySQVUv1Pmb71q435t6G1KXuXrnTl6z8Nwy71m/v8p1tksnTbKS3gT8mLAEdbKZHZ87fijwPeCJuOtEMzs5HjuELOHFN83s9IH2Jw2Yy5aRJObeYJKVNAWYamZXx12nA+e58ufEclNjuWvi/jOoJgWYSCSGAx1y+okP+z8D3gg8Dtwi6SIzuztX9BwvuBLPnUZ4YN+JoIE/I547kCQVySQ7SNwF7Fiw34u5b0dwBOq3mHs8f4v8+U7M/Q0x5dbFdFbMvT8kkfVEYjTQpWqv1uwCPGBmD5nZYoKk5tsr9uJ/gMvM7Pk4SF5GgdpYu6QZ5uBwBWEmeVg0Kw62mPvE3OEiMferWlRZE3P/ZgUx9z6Y2WxJL0h6bUxM/X5CDtB8uRclvSjpNWb2T8JDREsaPFqdaarbbZeZDpe16bUKg50aLFFOmUl/xUWZtsfc8av0u35D9TZEeQot348XJ61VWMZ/p8u+34u78//+xLaLNXN9fwZDS7aq0k+0gB3mdp1U+72MvJygrV3jcaAo5n3/aLX7F/C/ZvbvknPb1c3uQxowB4GRLubehEOAX0qaBDxESDJdxAeBUyQZmYh7IjEi8APhYOSbHPJUXMP0PgoD4M/AWWa2SNLHCEtBgybdmQbMQWIUiLlPLtg3E+jjvWBme+Tez6AxDdgXq7abSCSGNh30gH2CxoiBtcmce4D671aNk4HvunP3yJ171UA7NAoffxKJRCIxaHR1VXu15hZgY0nrSxoHHAhc5AtI8rbsfYF74vbfgL0lrRzD2/aO+wZEmmEOQUaLmHs7jFu6oL69eEy2ZrPCwkwBaK4LJZm8MOveSxOK16PKlFqGSrjJVvdkvw2dVB/yVMkN6cNyqij9DEZfm621lakS+TW/shyQftuvW05cMre+7fNTlrXr8fdrqbI+5PO1+vAOr8pTtlZZdm1l977KZzIoovnqTJ1mtlTSpwkDXTdwipndFUPWppvZRcBnJe0LLAWeBw6N5z4v6RuEQRfgWDN7vk8jbZIGzCFIEnNPJBLDFXVQ6cfMLgEuye37qts+Cjiq5NxTgFM61hmSSbYtJK0p6WxJD0qaIekSSZsMsM5HouLOTK/+MxjEtlZtcvyl3PtDJZ3YobZPk/SuTtSVSCSGMOqq9hqGpBlmRaK6zkjXhx2yLO3OTFCTFr1Y354/fmph+bIwFK/I4s1uXb1Z+XmuzmYmqzJzZtn+KmLq3gx75+b71re3uPfiwjqreGTmr8GfUyas7re92Xvx2BVce1m9Xux+9qRMeUYd+spOWNLwLEdPV/bT5cMqpj2b/SvOWXm9rIwz45ddc4MCjlMMKhN099+lsu+bb6vZ5+D7UXa+v04voO5zes4fN6W+7e/ZvHFTC9v1/0uBDRkwI1h8fXgO88uHUakP69pYT9IVCjqxl0taJ+4/TdJPJF0v6aHaLFKBE+M1/ANY3dX1VUm3SLpT0knxgSCRSIwARrKWbBowq9NKH3YHwqD6AzcAbAz83My2NLNHm9R9paQ7CYH+R7v9+fO/EvVYtwFeF8UQajwb+/ALgsoPZPqwWxJmx+u0uMaJ0TQ8U0En9lh37KeE2fU2wJnAT9yxtYDXAPsANa3H/YBNCUpEHwB2c+VPNLOdY0qvifG8PsiJr5959rktup5IJIYEySSbaMJI1oc9lKDFCCF+tFbvGWTxTgAXRnPx3VFZqNb2WWbWAzwp6Qp/vZK+CEwCphGkBP+c74wPbH7s/nusZuicOyFbhvXehd7kNXviGvXtMg/LBWNXLNxf1XOwrFzZ/ioi5t6b0Zth797srfVt71XbH49ef06ZSozHex97/HUunjSxcL91yOM476lado+fXW3z+naZMH9vd7H509N4j4rzajb0xw0A/p6WmV3zx/zn3pCb1eXTLDPP+nP9dZZ9v335vAd58SfdJiPYYJQGzOrcBRQ5rXh92CUKiZf7rQ8rqaYP+wzF+rA7m9kLkk5j+evD5tuGFmZfSROAnxOSU/9b0jE0XkcikRjOpHyYCYI+7Pg4wwMGXR82T5E+bCtq+rCoH/qwOa4nBA5DeEi4tkLb75HUHYOL94z7a4Pjs5ImU/wQkkgkhivJJJsYxfqwNT4DnCrpC8B/KdeJrXEBQdPx7tjuDVAXX/81wZHpKbLA4qZ401TDfmde8ubZsgDusmD2oYjv3/IUa69ynwb7Xlatv8wMW1ZXFZNxFbN3mZd0g/drbmmgrF7v3eq9dbv99xgviLAk2+++32X1e9Nu2f/VgBimDj1VkFnn1eoTiU7z8IMP1L+oZWEVZYmiywbM4cpwzm6ywYZ9wxYeevDBjtVfRblosKkSbpQ/5vHhTn7ALFvDnLA0W/lZOCYL+ymj2YC5/oYbDfimLbzoZ5UGlQn7fmpoP7EWkGaYiUQikegcI3gNMw2Yy4ikDzswyrwNy8xO/indB3N7yrxnh4qptuyal7V5too4wmBr8VbVku125kk/e2o20ysqU9Z2le9GwwzOWTr8TBAavVh9vWXatUu6i33jlnTnf1YCZRaXMh3ajpG8ZBMDJenDJhKJUcEwdeipwsi9suXMaNOdTSQSCaCT6b2GHGmGOQgk3dnO0+DZWMEj0WvPVqlzKFJm/vQsC/Os70fpPRvkWUUnvWTL6p20ZE59e/7Yldpuu4Y3f47rWVjfLhMSaNanKg5EZam7ykzjDXUOxufWNXK9ZIfnMD/0GVW6s5K+ELVhb8+19T5JN8cZ8a+k8B8s6SVJP4ptXS6pIwIjiURiCCBVew1D0oA5OIwG3VkAJO0d296FsDa6o6TdJW0OvAd4dZTb6yEIHgCsQEgAu2W8jq+V1F3Xkv392edU6U4ikVjeJJNsokOMJN3ZGnvH123x/WTCALoNsCNwS3wmmEiQ+4Nw7bUR8HeuPw14LdmHHnywbmMuM1N5j79F3Vn8WjMPy5HE8hQ3GE6UmVi9GXZA9Tsz59IST13o3PeyXS/eQReZGKazxyqkAXNwGE26swKOM7NfNeyUPkNYwy3Mhp5jdIxoicRoIHnJJtpkNOnO/g34UNSFRdLLY98uB94Vt5E0TVLtervIHijeC/yzYluJRGKo00EtWUlviv4ZD0g6suD45yTdrSxP77ruWI+ydIUX5c/tD2mGOQiMdN1ZSWOIs1Qz+3tcr7whml5fAt5nZndLOhr4u6QuYAnwKcIAPw/YJR5/hrDW2ZSGjPZuQuoD5n2gdldvVt4Hdg8nabyBmM7KzLP5Y53qx1DxOB4Ur88KTF6UrWDMGz+1vu2/b1Wl8aqUGci5vh9r3XNZ48ENP9Gy3pbtdshLNjoJ/gx4I/A4YXnnIjO72xW7jZD5aL6kTxDSDtZ+TxrSFXaCNGAOEmb2JPDugkOvKjllqwp1rley/5H8+WZ2aKs6zGw6sEfcfo6wFlmFLYG6AKiZ/Rj4cUFb55CtVeaPfa5iW4lEYjjRuTXMXYAHzOyhUK3OBt5OSOgAgJld6crfCLyvU40XkUyyibaQ9HHgLBo9dBOJRCJQ0UvWe8HH12G5ml5OsJDVeDzuK+PDwKXu/YRY742S3tGJS0szzCGIhr7u7GsHojtrZsVimRVpyERCcSaSqYv+U99ePCbL7OAZrlqy7ZI3wbbrQTvStGTLrmcgWrLeDOu1ZMcvmV/fXji2MZNImSZy2ZKD76u/homLM93kl8ZnrgdlWrL+Hr203nYNfepEJveqXrLeC36gSHofsBPwOrd7XTN7QtIGwBWS7jCzAaXGSQPmECTpziYSiWFL59aRn6BRxGXtuK+xOWkv4CvA68ysnhTXzJ6Ifx+K4Xfb45aS+kMyyQ4AJb3YRCKRaMC6uiu9KnALsLGk9SWNAw4EGrxdJW0P/ArY18yecftXljQ+bq8KvBq39tlf0gyzn0SFnqQXu4zw5qixvfWHSJZ0ZZZrbwobs2RBfbt3UvE/Z4PnrbuVg+VJW8W058uUJQweKGUetFvce3Fh/7xpr5dirdbB6muNfBqqBm1hZ84cv9SbQDPLf7tm4jKzKCUmXF9mbE/2/fSm2mmzH2loY+7kNevbPnWXv1Z/X/13vfE6i5NGN5inu7Ofen8v5k2Y1nBO6dNzG3TKU9nMlkr6NCF0rRs4xczuknQsQSnsIuB7BLGU86KX/mNmti+wOfArSb2EieHxOe/afpEGzP5TqBcrabKkywlxjGOBo83sTzH042/ATQQFnLdQHEPpadCLzZ8f45J2Jqjo/MHMvhbLPgKcDrwt9uEAM7s3rkmeRVg4v4E29GJrRIWhnxEEGOYDHwUeiPV9wcyuknQc0GtmX4l92Sk+AOwEfN/M9pD0OjLPWgN2N7O5+fYSicQwo4NKP2Z2CXBJbt9X3fZeJeddD2zdsY5Ekkm2/4wavdgcJwGfMbMdY70/N7OlwKHAL+J6wpsIcZ3N+DzwqRgn9VpgQb6A96I76+yz+9HVRCKxzOmgcMFQI80wO89I1IsNFxbUfHYjM39A9OaNppIzgL8ArzKzxcW11LkO+KGkM4HzzezxfAHvRffY/fcY0cTkTUreZDVxcZaeyZu7Ji96Pts/fpXCziyvgPc83sw3GN6mebwZ9u7N3lrf9mbbstRqnqFy/6qk96pyX/33qix9ljfbTlySGUfKvFn9dzJfrgyviez75FOFtevV7U3s3szbKZKWbKKI0aQXW6MLeLGJesbWwIvA6m7fUjJLRr1/Zna8pIsJpunrJP2PmfVH+SiRSAwlhsjD02Awcq9s8BlNerEAmNkc4GFJB8Q6FB2dkPROYBphFvtTSVPjaY8Q1lwB9nfXtqGZ3WFm3yF4w23WTl8SicTQpFfdlV7DkTTD7CcjXS82MkmSN5X+kDCD/kXUgR0LnC3pCeB44A1m9m9JJxIceg6Jbf5G0jeAq1xdR8QHil7CbN0rdPShzMzlmeeCtic58+z8cVMKyy/rf9p2TWfLon++T2Xes1ve8+esfMnsoWeQf0qq3guvG1wmSlBFsMKbKsu8m71ps6wPDf3O3boyU7c/x3uEe+/tsT0L69vjl2SGq7kTMj9XL9xQxtR5T+b2bNjynJaM4BlmGjAHwAjXi8XMyr75byrYV48/NbOfuO1r/TG3/zNV+5FIJIYPaQ0zkUgkEokKDBUHsMEgDZjLiWGgF/uGgejFdpoyr8UqptrGwPNsc8KSTMjIn+tNuM3MqGWmurL9/hrKxBG8mW+Fhc67d+JqhXVW0XnNX4M/x7fny3kz7F2bv62+vf2d57n2svITnJ7pnImZz5c6pK3hPyuAnq7sO+CFEqY9/0DWj6mZ+0CZmbTh/lXwqi37bH3945ZmEVLqzq6/mYm07HMoM0X7/d4M6/s3eWH27+uXK/xntXjcgGSdi0kzzESnSXqxiURiJDJcHXqqkAbMRCKRSHSMZJIdRCStCZxAkHh7EXgaOMLMmuqxtqjzEWAuIQaxmyhPN9C+NmlrJzN7tuS4AWea2fvi+zHAf4CbzGyfwehTQR/2AD4/0PaigMJaBFWe8cCPorjAoDOmJzNnqqt1+qMxzizWO26lwjqXdmdm0YZ6KnqzDobXqy+zuEQjtKEPFdJTNTunTBvWl/Fm2Nu2OqC+vfXdF2Z9HZMF2Df0o0M/nv6zgvJ7uWiFTJhiqTOBlplhvXm/zHu2zFzqPbG9ibjhM3TmYi9uAI1p58pM+t4s73VfV5z/TFbesvLPr5gl91ji6vdLAN48u8oLmQk7sD0DJplkB4dRImA+D9hK0kQzWwC8kYIUNc2QNCbKzw0FDjaz6ZKmAQ9KOq2Cqk8ikRgl2AgO71/eV1YoYA7cJulySbf6FFeS1pN0n6TfAnfSmCutjAYB8/z5kn4R9UrvklTXP42pr77u+rBZ3L+KpL/H8idTTcD8EqCmO3YQQQC91s4Kkk6RdLOk29y1HirpIklXEOTxJks6Nfbldkn7x3J7S7oh9vO8KF+HpDdJulfSrWTSeM3a2zLumxnr37jFNU0mPAz0tOhH2X28JLY1U9JsSYfkG5DTkj3j3PPzhxOJxBDEpEqv4cjyNsm2EjCfo5DL7EZJtTxoGwOHVNBkvTLOIjegMVay4XxJXzGz5yV1Ewambcyspsf6rJntIOmTBBm6j5AJmB8r6a3Ahytc59nAVyX9hSCUfgpBcBxC4tMrzOxDUR3nZkn/iMd2ALaJ/fsOMNvMto79Xjnem6OBvcxsXhRQ+Jyk7wK/Bl5PyCRyjutLWXsfB35sZmcq5J4rsx+eKWlRvI9HmFlPWT+AY8vuo5m9JV7HjsCpwIX5hvJasrUQ7nEuaLur16XoIjMWzF5hrZLuu/oHwXTYp96B1ON+VDpVZ582KtTr++HNsHds8Y76tjfbDgZLusY3eBmXUcXsXea57L1YfZkys61P3eXNvN5LdpXZDze0/eJK2TN+mQm81+333tG+ry9OfnnWJ2ck8/0ouxf+/+f5lRuFClbMF+4HaQ1z2TOiBMzN7HYFpZ6DyKWqIQgJ7CupllFkAlkWkcvMrLaIsRchgWqtzhck7RP7e114NmAcIc3WZsDDZnY/gKTfATUJv7L2bgC+Imltghj6/SWXUzPJrgZcL+mvBA3Zon7UKLqPtcSuZwDvNrPZJe0lEpUGy6GOHyxHMslLdvAYTQLmFwHfJ6ju+JQZAvY3s/t8YUmvpPW1ijCoHpQ7d7sW5/RpD7hHITb0rcAlkj5GSML60Xj8Lb6wmf03mntfSXAC6tMPR5/7GGf0ZwPHmtmdTfqbSCSGEcPV3FqF5T1gXkGYSR5W87bU4AqY51PBFwmYX9WiypqA+TfVnoD5KYRMH3coeK3W+BvwGUmfifq025vZbQXnXwZ8CjgiXtfKwI3AzyRtZGYPSFqBMBu/F1hPQeD8QcLMtml7kjYAHjKzn0hah2AKPoGQLJrYJm57EsGl7rsETdo+/Wjh6Xw8cLuZVUp06b38vIfg3ElZkLw3BXkzVdkT79KucUxaknk61sy745nfYG4rM1u2K1xQxVzmy0xclE26F08q9qg0VD9HVBMv8PfGz9z8uV4btiHQ3XnDlnnP+pRhnRIuyH9W3iu1pzvbnjQ786dbOq1YV3Ziw/Vkz8dLKRaT8N6tvV3Z5+b1Zv39XTRmUt3sOW+F1ese3hMXz2W+89huSLOlrK4G864zny4ck3lNl6UT8/d7vLtOf+6kBZnn7ZJx2efZKQZr+WAosFyNzWZmwH7AXpIeVBAFP45gttxJQcD8A/RfwHwmcCVRwLyg/VlATcD891QXMN899vWdtBYwr7X1uNdYdXyDIGJ+e6zzGyVVfBNYWdKdkmYRTM7/JSRuPkvS7URzrJktJJhgL46zwGdcPWXtvRu4M96zrYDflvTjzFhmBnCamc0o60fTGxJm9ns7x599W5TvOEWDJVBpsBwq9EfpZzjiP6uhjh/kfDjU/JLwppGGqavSazgis+LYo0RiKPHQgw/Wv6grv5QlUCmbYVaRFxvogDnY0ngrLvhvfXv2pCz5cCel8UpnmM7RZcWFWYhxQ7ylK191hrneRn10+HnowQcL++3JD5h+humTLK/x9Kz69pxpG7h+u3jIkhmmj5n0VJlheqoOmGWJnAdjhvniCi+rb0+b+++s3dwMc7Utdhnw09Vj999TaVBZZ+PNW7Yl6U2EzEfdwMlmdnzu+HjCw/2OwHPAe2KiCiQdRXDK7AE+a2Z/a+MyChmew3wikUgkhiSdmmFGP4efEZbKtgAOkrRFrtiHgRfMbCPgR8B34rlbEJwktyRkV/p5rG9ALO81zAGhJGA+avCzthcmr91W+TIWjC12oh+o0k/Z/rJZpcfP7MpmlZ7+eCSWKdGU4cXUy/rhZ5V3b/bW+rbPsTkQ8p9VWT+eXX3L+ra/l56XnNJNmWB/s7ZrjOvJwkf8Z+tngrjtfFtlM1Q/mHhRd58b0/fJz1T9NS8aU7w+6dWA8v8nq+UL94MOesnuAjxgZg8BSDobeDtwtyvzdkIOYoA/ACfGcMK3A2eb2SJC0vsHYn3ee79thvWAmQTME4lEYmjRwTXzlwP/du8fJ3jlF5Yxs6WSZhOiEF5OcIr0576cAZJMsgVIWlPS2dERaUZUpem7+NJenY9EpZuZcupFg0Fsa9Umx3tiP+5UUOXpvKtcaOc0SUVhQ4lEYoRSVelHTskrvg5rXfvyZVjPMAeDOJ0f6fq2C8xsu9jmmQSVnx+2OknLUdO2u3dJfbssr+BY5yAxbmm27U1ww4lGYfnlFwzemE+0+Bnbl/Fm2Hs2awjfZYMl+fDfzuL7EfIeBMqcsgZiPpy0MNMs8cpSZSIL+baqhBl5vNnXtzFhSRauXeW7XnZupzCruKThlLxKeIJG+dO16avDXSvzuEJiiykE558q57ZNmmH2ZbTo29a4FthI0jRJFyroyN4Y42GRdIykMyRdB5yhEs1ZSR+I72dJOsPVv7uk6yU95Gebkr4g6ZZ4ztdJJBIjAqOr0qsCtwAbS1pfQa7zQIIAjOcioKZD/S6C7KfF/QdKGq8gULMxcPNAry0NmH1ppW+7A2FQ/YGySP6NgZ+b2ZZm9miTuq+UdCdwNUF7tUb+/K+Y2U4E3dnX1QavyLOxD78gxDJCpm+7JWF2vA4ViE9kbwbuIMSX3mZm2wBfpjEOcwuCTuxBZJqz2wE7EZ7stozX83oz2xY43J27FvAaYB+CWAGS9o7XvAth7XdHBRnEfP/qJpvfn31ulUtKJBLLmV66Kr1aEa1ZnyaIrdwDnGtmd0k61sVt/wZYJTr1fA44Mp57F3AuwUHor8CnzCp4eLUgmWSrM5L0bSdG8QEIM8zfADcB+8c6roiz1lrg2EUxNRkUaM5Kej1wnsWcoE7/FuDCaGK+W0FNCYKe7d4E0QgImU82Jqgo1cmLr7e4pkQiMQTopFCGmV1CTn/bzL7qthcCB+TPi8e+BXyrY50hDZhFjAZ92/oapmu3Wfl6/8zs9+qrOduMRW5b7u9xZvarqh1eZ+PNqxbtw+qtiyQGiSprlhtsuGHLMkOTrN/TmpRalgyF7/pIUJYqI5lk+3IFMN57bGlw9W3zFOnbtqKmb4va07f1XEt4KEBB6/ZZM+ujRyanOUtwWtqGcM8OUIhHRSG5dDP+BnxIWc7Ml8d7kkgkhjlmqvQajqQZZo4oSL4fcIJCXseFwCOE4NifKOjbTqf/+rY9BC3XI83saYW0X779WZJq+rb/prq+7VkK2rDXU1HfNscxwCkKWrDzyRbS87wbeL+kJcBTwLdjvs5vAVfH67uNoC1biJn9XdLmwA1xZvsS8D4aNW8TicQwZCTPMJOWbGK4kL6oicTgM+DR7s4Hnqr0v7rVRmsOu5E1zTATiUQi0TF6beSu9KUBs8Mo6dsOCg8/+EB92wdel2V5aDc3ZmLZUOTgc/HYTevbA9WeLcsUsywZCn3oL51wwOodZtfcDmnA7DBJ3zaRSIxmhttDQjuM3LnzIKORrzf7lagcdHvszyurnDeQNhOJxPAneckmGogKPyNWb1bSqwjKPDuY2aI4yLXOTbWMKEvbVGpuHabZ3UcLZdqznUoNtqypkky81bEa43vm17d9ouwJS7PQ74Z0YhXqH2yTcZphJvKMdL3ZtQhxmIvitT1rZk/6ApImSrpU0kdVrkNb2mYsPyMeG/JZChKJRDVG8gwzDZj9Y6Trzf6dMCj/S9LPJb0ud3wy8GfgLDP7NeU6tM3a/JCZ7UjQo/1sTfTAI6cle9bZZzfpbiKRGCr0Wlel13AkmWQ7y4jQmzWzlyTtCLyWMPCfI+lIMzstFvkT8F0zOzO+fw3FOrTN2vxsFIiAMOPemJCWx/ejriX78IMP1O1IDR6wzrw0rmdBfbs8m33ykh3KjATzbFnaLuVCiauYLr0Z1lNmhm23/sFgsHMTLk+G5zC//LkL2LFgv9eb3Q54mgHozcbzt8ifr0xv9g1xVncxHdabNbMeM7vKzL5GyBiwvzt8HfAmN3tuiyi9txfwqpjd5DYa+59IJIYpySSbyDOi9WYlbaqY5zKyXa4fXyWsr/4svi/ToS1rcwrwgpnNj2usu1bofyKRGAYYqvQajiSTbD8YBXqzk4GfSpoKLAUeAPKOOYcTtGe/S7kObVmbfwU+Luke4D6gpam6qySVnd+/uHtifduLFeRNYTW8CdeXKTODVaXMC7HMVFd27oQlL9W3F46dXFhnfzwe/Tll99X3z/djaXfmLO3bG9ezsL69YOyKlfrRijLzLMDWd1+Y9akr69OUef+pb8+bmC2Le8/qdsUrqnyeZZ/b4jHZdzK/TDBlwdP17bkTsmgrL8zh++qXInw/xvZmCYH8/8CkxbPr2/PGTa1vT16YrXwsGLcSnWa4zh6rkAbMfhK9Rt9dcOhVJadsVaHO9Ur2P5I/38wObVWHmU0H9ojbzxFyULbEzGYAu1Xo4wfd9jsKyjZrs8qsOJFIDDN60oCZSCQSiURrhqu5tQppwFwOJL3Z9lGJToM3x3lT1goLn69vz524WuG5zTwYB0LZD4a1KaDQ05X9ew7Wj1CVen0/ysyZg91Xb4IFuGOLd9S3vel2qTOBlpkzyzSHu3uX1Ld7usbWt/13z0rqlKtz/MLMFLp4sjORLmlML7twXGa69nX5fpf1w39fvbnZ33tvPvcs7S7WX+4UySSb6ChJbzaRSIxUlkXGSIUk9ecA6xH8R95tZi/kymxHiEVfiRAx8C0zOyceOw14HVB7ujnUzGa2ajd5yXaQkaovG+MqZ8bXU5KecO8rSeZJOlTSiZ3vdSKRGEr0okqvAXIkcLmZbUywkh1ZUGY+8IEonPImgpPmVHf8C2a2XXzNrNJommF2iBiTOCL1Zf1MVNIxwEtm9v3BaKuMUtOU2/bm2ZcmFHtIenz5ZUEVU6Uv4z0eB1Jns3OqCDlU6UdP9+D+lOQ/qzIP2i3uvbi+XW4aL77msu9DWXlfv/+OvbBSJmjlTZ7zx1bzSG23H/4yX/bQtfXtJzd4bWHxRWMyL/DxS+cXlhkIy8gk+3aiQyNwOnAV8KXGfti/3PaTkp4hxMm/2N9G0wyzc4x0fdk+SNpR0tVxNv03SWvF/Z+VdLeCtmwfTTtJb5N0k6TbJP0jxpImEokRQK+p0muArGFmtRiip8gU1QqRtAshgcSDbve34m/UjyQVS4PlSANm5xjp+rINSBoL/BR4V9SEPQX4Vjx8JLB9VCH6eMHp/wR2NbPtgbOBL5a0kbRkE4lhRq9Ve/n/7/hqiPWOD9N3FrwalqXMzKDcay8+yJ8BfNBZ4Y4CNgN2BqaRm52WkUyyg8+I0JctYFPCQ8JlcfzvBmpPfLcDZ0q6ELiw4Ny1Cfq0axGe+h4uasBryf7n3plGDAjv7l1aL7PAeRpOnZNpMSyYOK2+7c1iS7ozBT4f5N69NAv+9ia1Zp6tjd6TxV6Y3uPRm7+8WczjPX2nPZtZ8p9dbfPCOr1nZ5kHa/4a/Dm+T/78Jc6TctrzD2T9XmGVwvKTZj+R9XX1LevbZd7Nnioeyv6zgkZvWG+GvXuzt9a3d73t1Pq2D9Av80Jt6FNJv/299J/VuKULioo3/f74e9xwLxe9WFh+0dhMP3bc0kwowpxC5dPrZ2HgL5txQX37Pzu+o7695sPX17efXXen0v71l6omWf//XXJ8r7Jjkp6WtJaZ/Sf+jjxTUm4lgnToV/xvpZudLpJ0Ktkkoilphtk5Rry+bA4Bd7lF863NrCZS8FaCbN4OwC1Sn0XEnwInmtnWwMdIOrKJxIjBrNprgFxEpih2CDm/DoDokHgB8Fsz+0PuWG35SATRlTurNJoGzM4xovVlC7gPWE0h2TSSxkraUlIX8Aozu5Jg5phCkNrzTAFqU5JDSCQSI4Zl5CV7PPBGSfcTEjkcDyBpp+iPAUGJbXfgUOfVv108dqaChOkdwKrAN6s0mkyyHWIU6Ms2YGaLJb2LcG1TCN+lEwgewb+L+wT8xMxeVGNik2OA86IJ+ArCQ0BTvPnKa5V6Hc05k9fK9vcU62t6vNaoN8FV1Rot81psCJhv8HqdULjf4z0k56y8Xss6vUmxqsesP6dMo9abEudMzZ7xlpaYMJdOyz4f7zEqtZ5KVOm3/6yg/H54M+yN22fKjVve8+f6drdlJv2yz72rxEzsf+i9NuzScdnnNnlRJpoxz31XJy6Z21CXP9+b8eeNX7lwf4MpedzYwjL+3r+4zRvq2/4eeTNsFQ/odlkWcZjRc/8NBfunAx+J278Dfldy/uv7024aMDvISNaXdecf47ZnEp7g8rym4LzTgNPi9p8oMKEkEonhT09vUvpJJBKJRKIly2KGubxIA+YQQUlftillwdxl+xuEDkrSMzUIGnTwobjMxFjF1NtgwnWeoKVB+AMULij1rPWmxxJvzobyJd6gndKVzYtPlLXnvWG9Gfauzd9W3/ZetVXaa1dn2C8Z+H7OHzeloZxfTigzz5Z58eJMyfm0YTX8EoCntM4OkcTXE4NO0pdNJBIjgd40w0wkEolEojXJJDvEkbQmwUNzZ4JO4NPAEV5LsB91PgLsZGbPdqCL+bq/bGbfdu+vN7PChM0DaGNVgpDAZ7xcX5t1rAf8xcy2yu3fA/i8me0zwG4i6VDCff50s3Lje7IA+4kLX6xvL5gwtb5dZkbznpDeK9AHnpelV2pGmQejb9u34fvX4JHp6ik7t7e72Cu0zFzYzCxWltKp7BrK+urbmLg48wB9yXl5diptWv4zaUzR1V1YznvDlokb7P797CvctUumvdr9yH317SXPZDHxYzfbor791IZZ+Qazf4m5OH/fq5guvbCEN7GO6ykWLmj8fmcCFX7pYkIUABksRrLTz7CPw4yBpxcAV5nZhlGm7ShaaAvWzo1xg8uaL/s3nR4sIwcANwIHDULdiUQiUcgyEi5YLgz7AZNlI3qOO/eKKNh7uaR14v41JF0gaVZ87Rb3XxiFye+qCRpIOh6YGINoz4z7Xop/Jel7US/xDknvifv3kHSVpD9IulfSmU6PtoyDgP8DXi5pbXcNL7ntdynkhSu9Bld2AwWx9J1z+3eRdEM8dr1CRpVaOq/zJf1V0v2SvuvO+aCkf0m6GXh1k/td15r83Tl/KCuWSCSGECN5wBwJJtlWoudzonnyRkkXxWMbA4dU1HH1/JSQvut0SR8CfkKQVfoJcLWZ7Sepm0zZ5kNm9rykiQSJuD+a2ZGSPh1l8vK8k+Bwsy1BfeIWSdfEY9sDWwJPEkQJXk0QMe+DpFcAa5nZzZLOBd4D/KDFtRVdw8qxvk0JIumHRoGEPdx59wKvNbOlkvYCvg3sH49tF/u9CLhP0k+BpQTBhB0JyVuvBG4r6pDXmnzs/nvqBq35EzKTX5n5y5s/x9qi4jID9Bas4mE6piczqy4dW+zRW8UU2qhb27rdZjTTNy2iLDDem4wXj5lQWL6qebtdSj/3kjRwHm+Gvebzf6lvb3HvJ7JzV9uCIsq9gf12scm8z70o+dw9XijCn+/3+z75tsv0ir1YRZl5fiAkp5/hSSdEz/O8iky4/AygNmt6PfABADPrIcvi/dmo/gNhJrsx0Cw04zXAWbGOpyVdTViXnQPcbGaPA0iaScg0XjhgEgbIc+P22YRMIq0GzD7XIGllgg7un4B3mtndBedNAU6XtDHhd8OPQpeb2ezY57sJsoCrEszn/437zwEGlGQ7kUgMHZZRPszlwkgYMO8C3lWw34ueL4lOPP0SPe8PcRa2F/AqM5uvkGFkICLjfprUSkD9IGBNSQfH9y+TtLGZ3U/jw3CV/swmSOa9BigaML8BXBlnpusRErn2p8+JRGIEMFzNrVUYCT9gVxBmkodFE17HRc8d1wMHEmaXBwO19OaXA58g6MjWzJlTgBfiYLkZsKurZ4mksWa2hEauBT4m6XRCjrbdgS8Q8rZVQtImwGQze7nb93XCIHosYea6OUE8fT+g5t5YdA0Ai2O5v0l6ycx+n2vSC6kfWqGLNwE/VhBImENwTprV6iTvFbhwTJbmqErAfD4ovGbaygfD18xiwjoWfO2D2MtYcVFmdJg7PtNMLQtI9+T7OWnJnPr2/LEr5YsXntOqTJmJ0Zs/l1Jsbh4suks8QD1l5mDvDevNsN57tkzcoOr3osxLu8z83lCv99guqbNBS7eCV27+u+7r6bTQQE/rjG7DlmHv9BOTh+4H7CXpQQUh8eOAS4CdFETPP0D/RM9vl/R4fP0Q+AzwQUm3A+8HDo/lDgf2jG3NIKTf+iswRtI9BCV9bwI+KdZ9Zq69Cwi5JGcRHgS+aGZPtdnng2I9nj+SecseCfyFMPj7JINF1wCAmc0D9gH+V9K+ubq/CxynIPze8gEs5qE7BriBsBZ7T6Wr6hBlP6KlP17DiCqDZWLwqTJYDhUG47s+kp1+ZMO154lRxVP33lb/orY7w6zihLI8B8yyGWa7DNaA2RjzODAJvA023LDPvocefLDtPnmHIz/DrPI5rvFMpjb5zOpZiHGnZphVB8yyvpad7+992X5fj49D9TNMvz9/PetttMmAv/wnX14t+PYjbxh+T6YjwSSbSCQSiSHCSJ6DjfoBU0NE9Lw/SLqAvrkkv2Rmf1se/RlMvMqJzyvY1Zs9Lc+dsGp9u0zY2jPQ8IcqbXi8WtGi7szl388q/bX5sIAq/evPrLJMWag/66fLkrKQID/z8jktG2ZYTsXHh4+UqQF5EXcqhOSUzf7yNCg5lagVlc0eKZ1ttv6elN2jTtE7gtcwR/2AOVREz/uDme3XulQikUgsO0bygNnycUnSmpLOjg41MyRdEj0xB4SkR6KgQMeR9OXc++s7XP9VCmpBsyRdV1O3KSm7nqQ7O9l+i76tJ+m9HazvKknT3fudYohMv/sUVYBO7FQfE4nE0KHXqr2GI01nmFJdp/V0Mzsw7tuWIADQUtg8ni+zEsmNwePLBMUZYNC0Wg82s+kKknffA/Leo4OKpDFmbvU+Yz3gvUA+/GMgrC7pzWZ2absnShrTiT55U6W55zyf/9CbWLudqbaKSdY7QlQxR7ZTroY3w5bhVVjKhLcrhSY0ocyppEy8vkzQ3Jf3n0+VUJp2ySvhtG2GdOd7MfUy2s2l2fjdy0JevBh6d+7f1X9/fP98XX7bOzc1JA4oyW9artCU9c9aKmy2T3VH0v63LWkacA7ht+UR4N1m9kJBuR6gtrz2mJntG/evTxB1WYUQFfB+M1ucPz9PqxlmoU6rmV0rabJGt1ZrjWuAjWLfr43341bltFhjO4fGPl8WZ9iflvQ5BR3WG+OXAEkbKmiwzoh1bhb3nybpl3Hd9buSXhevc2asY0VCCMtr477/lTRB0qnxem9TiEltqvVawPeArxRcT7O6L5J0BSG+s6FP8fSXVWw7kUgMI5ZRWMmRBCWxjQm/MUeWlFtgZtvFl5/UfAf4kZltBLwAfLhKo60GzDKdVsi0WncgDKw/cIPMxsDPzWxLM3u0SkciNa3WbYAzCfqmkOmcbgvsQFD3gaDVuiOwE0GGbhUzO5LsJh2cq99rte4FfE/SWvHY9sARhPjDDWgiCp7jbYQnmGeAN8b78R7X9zxbxX7sDHwLmG9m2xPiEj8Qy5xESMu1I/B54Ofu/LWB3czsc/HYp6Iu7WuBBYQvzrXx+n8EfIoQrro1IRbzdEm16cp2sa9bA+9R0KAt4gZgcW1AdDSrewfgXWb2uoI+VWpbTnz9t+fmQ0sTicRQpLe32muAvB04PW6fTtD0rkQcp14P1DI6VD5/IE4/o1mrFeBMSQsI5oDPEDRUT5S0HUEGrmyd90ozmwvMlTQbqNl+7gC2kTQZ2A04z01yve3vvNh/CIH/P4wz6fPN7PGCifFrCA8imNm9kh51fSvSev13Sb+/CRwNfKli3ZeZ2fMldVVq24uvP/6vO+sS6t5zsEzlpafLia+XeLMOxUByT5kZ1tMfT9Uyb8uyMlXiBXu7Bvde5q+zVIC+QSWn+N74nJZVvFDLvGc3v/eSwnPLPHirft/KTKyeMvN5lfb8/8ZgCRcsA9aIIigAT1GeznGCgg/GUuB4M7uQYIZ90S1pPU4Yv1rSasAs02mF0a3VCnEN0/XnGELi6m0JM/eFJef5dnrd+97YZhfhw9yu5Pz6vTWz4yVdDLwFuE7S/7Toc7O+NL1mM7tC0jdplPhrRqvvQNKZTSRGIFWl8eIy2mFu10k1edN4/B/AmgWnNiwPmZlJKhum1zWzJyRtAFyhoGQ2u6RsS1qZZK8AxtfWByHotEp6LUFDdLC0WqFYqxVJ3ZKmUEGrtaD+awnmv25JqxG0Wm/uQL+J/flPdHB6P9CvR24zmwM8LOkAqK+7bltUVtKGZnaHmX0HuIWgOTsX8F4X1xLuZU1ndh2Cjmx/+CbwxX7Une9TIpEYoVivVXuZnWRmO7nXSQ31mO1lZlsVvP5EsBCuBRD/FnpymdkT8e9DhMQQ2xOskFOl+jR9bTI97KY0faqPI/d+BEHuLxFmTY8Q1vrOBP4cR+zp9F+rtfY8ci7BtHmqpC8A/wU+GI8dDpwk6cOE2cgnCFqtH1fQar2PYq3WW3PrmBcQzL6zCFk7vmhmT9WcagbIz4E/SvpA7NtAZtkHA7+QdDTB1Hs2xQLlR8SHlV6CNeDSuN0jaRZwWuzXL+LntJSQ03JRgem2JWZ2iaT/ul1V674916c+3myt6HJekUtLTE2rP5slU5k7JVsSbfCYHV9ski0LIs/jZey8ObiKZ2gVj9YG0fNlYDIuM7GWyadNWpzJ780bP7W+7U3d43oWZOUX5j/qvtJ4VcjnbSwzpVYRX3hqw9cW7q+CN8Pes9lb6tvebDvOeTerK/ve5k2nvt+N8n4ZY3syQ4y/B17UokxYvSEfqKu08X7l8z8MnGUUMnIRcAjBofAQQgrCBhTSE86Pv0mrEvxSvhvHtSsJ1tOzy84vImnJJoYFT953e/2L6pPn+h+aKgPmS+Oz5NOeMm3SPIM9YC5PykIbqgyY/sfZ38v8gDltm76DVRUt2aoDZrsKT2X1lK0L+jJlA6YPB/Lf1aoDpu+fz9LT7oBZ9p32+/MD5ss23WbAX8zv/KHakPmld3X1uy2FbEfnEqxajxLCSp6XtBPwcTP7iEKkwq8Ik4gu4AQz+008fwPCYDmNkMD+fWYlmeYdad0okUgkEh2jdxlMMc3sOeANBfunAx+J29cTvPCLzn8I2KXddgd9wFTSak10GG8u9HkY1Zs9gY9xpqwJ856tby8ek/mFeTOvnyH5IPx89hD/ZO+D0qvQbrB9lZlQFSYvapzllc0M/XV7lnRn/77ew7JsFupnM7NXWAvPtIp9ztNXuKA4KH/c0swcvHRcsaWgcabfmrLrLPOe3fW2U7OTnRBFd242503/3rPWtzdhUeafYs4T2X9u3rpRJU+mZ/K8p1uWaZeRbLQc9AEzabUmEonE6KFnuOreVWDYJ5DuL+qwRq6km6KSzWOS/qtMgWe9Dnbbt9dHo1bSMZI+3+K8d0jaokWZPST9peRYRzSAFdWXEonEyMJ6q72GI6NyDTMqPfRLIzee20cftzaTlnQosJOZfXoQut4J3gH8Bbi7RbkhhTdnjVucjbXju4vH3YnXXljffmmPd9e3lzgxgAlLsnO9Y9C8cVPr23lToD/fm87KHHq8WazBg9PV481r3tzs93vzb1lKLm+a9O16E2z+HO+F6c/xZlhv5vQmP7/fn+sTfPu2yii7dw2mySWNn/P4hZmp8oWV1imsd/KiTDfDmy2rOPo0fCbuu+dNp94b1pthb9z+g/XtN1xyVNahZ3Pmz5Uzc//z62fLaSvMyyIkFk2YWt/2Dmve+WzlOY/Vt2evmMXfT3X7n5uaeSc3eN4uKQsX7z8j2ZF0tM4wCzVygdvUIX1cSV0KOqmrufcPSFpNmSbsdEn/krRPLNOtoHV7i4Ke7sf6e4GSPhrrmSXpj5ImRa+xfQmSgDMVNGs3kvSPWO5WSbX/rMkq19b9Yrw/N0vaKLb3tjjLvi3Wt0bcP1mZ3uztkvbP9XNVSTdIeiuJRGLYs4yk8ZYLo3XALNPI7Zg+bpyB/o4Y2E9QJZplZrU4xvUIXlpvBX6poMH6YWC2me1MkOz7qIKqfhkbOtPvTODj7tj5ZrZz1N+9B/hw9Bq7CPhC1HV9kBBP+7NYbjegJjfVTFt3dtSPPRE4Ie77J7Br1MU9m0zg4P/VykeN4CtqlcRB9WLgq2bWJw2EnJbsGeee3+Q2JBKJoYKZVXoNR0alSbYJndbHPYUQEHsC8CHAuc9xbhxU75f0EEGlZ2+CnmxNjnAKYaB+uKT+B72EnoI8X42tFKTspgKTgT6evQrZTV5uZhcAmNnCuB+aa+ue5f7WxNTXBs5RUN0Y5/q8F5l6E5al4BlLUHD6lJldXXRxeS3ZmmFs6cTM+9HH2nX3uPiynV5X3/aerd4E51NpVdFO7UODac/t9t6wJRqjZbqlS1UcL+fNkw3eld5D1MXslcWqAoxfkpkSvbm2rA11F5uGV5mdfS296RBnkq1CqT6t68PiMY0eyYsnZ+/LvInnlZhhPWVmWF++zBvaixJ4b1hvhr38LcfVt1/3z+83nN8zNqvXf14LJ2amV//d9YIQ3qQ/1rkD+M966bjsc/D32N/LJROy9HidoqdneA6GVRitM8y7gB0L9nt93O0I2rD91sc1s38TJJxeT5hN+nyS+W+VEQbsz1iWjmZ9M/t7u+1GTgM+HWeCX6d9nd1mWq9WsP1T4MTY3scqtLeUMMtvV/82kUgMYZZReq/lwmgdMAs1cgl6uJ3Wxz2ZYJr1WUYADojrmhsSTJ73EWaBn1DUwZW0iaT2HtczVgT+E+vy8oB1XdeYNeVxSe+I7Y2X1DrLcUjLVft7Q9yeQqbHeIgrexkhDRixjdrjsxFm3ZspyC4mEokRQG+vVXoNR0alSbaJRu4xwE80MH3cPBcRTLGn5vY/RhB+X4kg5bRQ0skE0+etce30v7SR5y3H/wNuinXcRCZ+fjbwa0mfJWgpvh/4laRjgSXAARXqXlnS7YRZ6EFx3zGElGQvEB5Iamuv3wR+FkNgegiz3fMhpGqTdBBwkaS5ZubzfjbgTVDeNJXPYl/HiRhMWvRiffulCZln4qTFmaelN+fOmbh6fbuZJFuZCXMgqbi8GbYszZM3I05Ymhk+GjxBm8i8LRybPYNNm/1IfXvuZJcYwj1Ke1OyFzd4caXM963Mu7XdFGpl/c7L3E1akkn0zR9bbFb0fZ0/bkrL/pX11X/Hyj6HBlEC5w3rzbBXv6Yx4muPa76T9Wly1if/XcSZZMva9l7C/tpenNQoGlHDm/FfmLx2w7Fi4cj2GK7rk1UYlQMmgJk9Cby74NCrSk7ZqmK9pxHMoTW2JTj75Afff5iZd9KpOQp9Ob5atfNIvk9mdozb/gXwi4LzriM48nhen3tfU/avnfNpt71e3GyYFcYMAn0EjM3sJRpnnLX9k+PfRSSzbCIxYhiuMZZVGLUD5rJA0pGEzCoHtyqbSCQSI4HeNMNMeFRRH9fMjieknyG3/9A22toaOCO3e9FwlhzsDz4I3ZsIV1iQZRsb/0KW0m72y7LJtzdxeZNfgwlzXGaDLPPUzFN2rF2Tny/vTY9+f1mdZVlSmnn6+vO9GbaKXq33sPRlvNmy7BqqUNbvKQsag/4Xjiu+bi+44Pvhs5g0eigXmzmrpAkrM5l7QQLvCetNsABX7Z4Zaba++8KsLvf99tlEvCetRyVTOm+u92ISfkmjit5su/RUzSA9DEkDZj9YloNVHIS3W1btJRKJxEAYwRPMUeslO+TQ8Ne27aM/GxWN3hW3j6jogZtIJIYx1muVXsORNMMcAkSP2JGubXsEIbxmfotyhTSkP3IesM+vlEX+jF9htfr2GGeGXTS2dWSON2tV9ewsM1tOcBqj3mT6shkX1Lfnb75rfdt7M3rTYZlAQZU+NMPX672Py+r15tYyndwlKjZbDsRL1jN3QqPef5kp0bdXZur2lJmAyzxSPV4kwH8/vS6sN6N6T1hoNMPescU76ttb3vPn+ra/95MXZvqxcydm3/XVn7i1vv3U2jsXtu2Z6JKA93Z1fggYyWuYaYY5NBjR2rYxhOVlwJWSroz79o4asrdKOk/S5Oa1JBKJ4cBInmGmAXNoMFK0bcva/gnwJLCnme2pkB7saGCveG3Tgc/lz2vUkv1ju80mEonlwEgeMJNJdmgznLRty/4DivbvSogFvS6O/+PIFIOyE52W7KMP3Gc1Y6U3l3lvxlX+9c/69kvrbVff9pqxHn+uN7st6h7YUmtZe0/u2DofuTfzeTNxg1iBS3VVpofbDF9XmRerv8elWq8lYgWD4XmZTxPm++fvmRepmOfSYVXy1nX9LjPtNt4LCsv49FxeF7ZBkIBGb1hvhr1r87fVt7e76w/17flO99d/N/778u1dn1p/tv6+eDNvp1gWWrKSpgHnEB70HwHe7XSqa2X2JNO6hvCbdqCZXSjpNOB1QE295FAzm9mq3TRgDg3uIqju5PHatkskPcIAtW0leW1bHx/aTNu2j3B7Ac/RVyhkGvBsQVkBl5nZQQXHEonEMGYZKf0cCVxuZsfHePcj6SumciUxwiAOsA8AXpv7C2b2B9ogmWSHBiNB2/Z+4GWSNo9l1yWoHM2Mx+satsCNwKuV5dJcYSAewYlEYuiwjLRk3w6cHrdPp7WE6LuAS82sX06HNdIMcwgwErRtzWyRpPcBp8b1zyXAR8ysZvI4CfirpCfjOuahwFlS3b3yaJp4BHsT2ViXxsqboOZskHkIjluUeQKOG5MFanuvVW++8+Yu75mY904tMz02eI+67QZtWFfem9Ty6beKzvVemD7lk6fMmzNvqvVt+za8KbrMW3eFhZmAhPfU9KnFvHhAu4zvyX7PfH/y3rY9JR6gHv9Z+fLjnRez9yT1buZVNIPH9mT3aMKiTJfYpzpr+Kxyn5sXJfDfOW+GnbllZnja8+TMIHTPaZnRZ50Tf1jf9lrJvv4Gj2t3zZOeeYhGXsNAWUYzzDXMrJa79ymypaoyDgR+mNv3LUlfJaQZPDLKdDYlDZhDhOGubRvLX0dYnyw69lNCCrDa+ysIjkSJRGIEUdWhJ1rUDnO7Top+C7Xj/wDW7HMifKWhvTDhKG1UIUfv1jTmBD6KMNCOIzzMfwk4tlWf04A5ikjatolEYrCpKo3nnfpKju9Vdiz6YqxlZv+JA+IzZWUJE5ELzOo56HGz00WSTgU+X3hmjjRgjgBGg7btSguy/4eunszUJGf+MWWmxzmTi1MbeWZPyh5efXqmMpEAaDRhLu6eWFjGm0a9udVre5aZZMvq9/u7fUC+Ex6o6iXbIDjgAvp9G2Vm4nkTphWW91ql3pxbVk8ZZR7Kec9bf//MmRu9SEWZaMJid8/KBAr8vSy7hgbzfFdWz0vOC9Xrtub7483B3lvVLw94M+yVHzmzvr3tXVmYlbcjlokvePOxv5fPvmKHhj41JvvqH8soZOQiQhak4+PfPpmSHAcRZpR13GArwjLTnVUaTQPmCCBp2yYSiaHCMlrDPB44V9KHgUeJy1mSdiL4YHwkvl+PIOxyde78M6OIiwiOiR+nAmnATCQSiUTH6IAHbEvM7DngDQX7pwMfce8fIcSv58vlcwBXIg2YJUhakxDgvzPwIvA0cISZNdV2bVHnZOB7BEGA2YRYx1+a2a8H2t+S9i4A1gcmE+I5a4IDnwS+b2a7tTj/y2b27Q7252Tgh2Z2d7vn+nROExe+6I5kprnZK2Rm2KlzH69vz5+U6ZB689rKrswC59noyZvjvJm0WQqtGt486T0my8r79rzHqG93KcUmwqpest6UOL5E99Z7InvT3orzM9P4i5Oz36GJS+YW1pMXHGhFWUqqPp6+Jdc6bmnW755xzpPWmXC9R2+Dl6w3mTqzZYO3sjPX+8/Tm4hXXJSZV73JvKzP0Ohx7Ovy3rDeDDtry/3r2zvPyky1DSZ6t0SxcExxOrQ1Hs3phWxSyZewKcNVxacKacAsYDDE0CMnAw8BG5tZbzQJfKhDfe7OxVViZvvFY3sAnzezfdzhpoNl5MtAnwGzxTWWUjOTJBKJkcsyMskuF5JwQTEdF0OPggC7AEfXBhoz+6+ZfceV+YITOv+623+hQsqvu3LiBi9J+oGkWZSHnxQi6aX4dw9J10i6OF7DL6OAwfHARIWUYGcWXWMUZr8z3ov3uPqukvQHSffGcxWPXRXXGJD0pngfZ0m6vKSPdS3Z353TliBHIpFYTvQs7an0Go6kGWYxrcTQ50QB8RslXRSPbQwc0kTfdUtC/GPhrEzS3rGOXQgL0RdJ2t3MrgE+ZGbPS5oI3CLpj9GGvwJwk5n9X38vNLILQdv1UeCvwDvN7EhJnzaz7WL/1vPXKGl/gvPPtsCqsV/XxPq2j9f7JHAd8GqgLvQaZ9a/BnY3s4ejbFUfvNv5s3feYCwOGqo+JdHiktRdYxZlequLVyz2/XvJmWrHL8lMgd6k2IwqXqneC3WV2ZkE79zJmYeuN7d6c6lPUeZNgT4g3YsE+DLe/Cesoa8TF2fm04Vji82e/n741FreK7nRUzV79vZB+BOWtKfg6M2wnl51N5ioGzyZ3cfgzZBl6b18mQbxiRJhiTItXd8H/51Zec5j9e2xyr6HAC+stE5hGz5Fl9eG9aIE3hvWm2Fv2TbzpN3i3ovr2+OWFItArHnt77KKpjWmTesEaYaZqFETQ78d+AcDEEOX9JU4e3sy7to7vm4DbiUIBW8cj302ziJvJMxea/t7gE6k8bjZzB6KJt2zKJf78Nf4GuAsM+sxs6cJXmg1IYKbzezx+HAwk6AW5NkVuMbMHgYws+dJDBr9yZk51PCD5XDFD5YjmZStZPQxGGLodwPbSuoys14z+xZBmqn2CCrgODP7lT8prj/uBbzKzOZLusq1uTC/btlPioTXi6g6XfAPwz2k71kiMWoYroNhFdIPWTFXEGaSh9WkmjRAMXQze0DSdOCbkv6fmfVEzdXa4//fgG9IOtPMXpL0coIe6xTghThYbkaJ9NwA2UUhz+WjwHvI1DeWSBrrFTIc1wIfk3Q6ISvJ7sAXCDPjVtwI/FzS+jWTbKtZpg+YL0t75b0c503JPDgbTG3OPLm4e2L9/Lw340BmZWXnPjeldSpRb+abP25KYZ1VxACa9d8H1pfRYIZ19/j5FbPleW/y9J+Jv4Z8W6u3bLmYRd2TSr2SX/bQtfXtp9fPlvLLBCi8CbzsPnkxhDIBBI8v89yU9UtFIMpSbj21dqYS6a/Ta8P6vvrvqzfD3r3ZW+vbPmWY58VXNaaZ80IO6xWe0R697fkCDiuSSbYAC0b4/YC9JD0o6S7gOOASYCcFMfQP0L4Y+keAVYDa4HkZ8MXY5t+B3wM3xPr/QMju8VdgjKR7CMG67ebArMItwInAPYTQkwvi/pOA2yWdWXDOBcDtwCzCA8YXzeypKo3FpNWHAedHU/M5A+t+dcrWo8rKJIYGzUIyhhrtDpbLEz9Ydopkkh2FDIYYupnNAT7W5PiPgR8XHHpzSfniTMV9y10FXNXk3Dm5kJNamS/RmGNuK3fMCDPKLzRry8w+7bb3cNuXApdW6X8ikRg+9FbUkh2OpAEzkUgkEh2jtzcNmIk2qCqG3uE2a6o+ni+Z2d+Kytcomn0ORbzCzKSnHqhvP7XBq+vbXiVm3OLMnX9pd2YW8+71K7h8mHKKQS+Nz9ZLm5lnq5gJ/fle9ceHOfh6vNluwpLsGnzYgl8v8yEcZWtt+Wvw53jReU9PiTD4kjHZ+p9vb7wLVSnL11mFsnXKSYtnN5Tzn+mTG7y2vv2yGRfUt1/cJlNO86ZHH5bj+1pmMi3Lh1kWVjPVhZUsHZeFybw4qTEhgP++esWhhtCikpyWPjTGh4/4dcu7Nn9bfXvzey+pb7/wxSPq2yt+/2d0muFqbq1CGjAHgWUphu7a3K91qUQikRhc2hQAG1Ykp58SJK0p6ezo9DND0iWSNhlgnY9EVZyZ8VVFni5fx3qSClPRSPofV/dLUZlnZlTnGRQkHSOpTy45ST2x7TslnSdpUty/hqTfS3oo3tcbJKXBPpEYISSnn1FGlHIbDC1ZgD3N7NlO9rdGNL/+LfbjKoJ+7PTBaKsCC5xK0JnAxyX9CLiQcF/fG4+tC+zbqjIvrD53gyw4wZupvErMwsnFijGeKuEVzWjXm9abYcvq8aEQ88ZNbVlns9ydVc7p6W59/jx3n8pCWsoUetql7J5WuRcA/9nxHS3rKruGsntZeo9LPv7npm7Ysg9Q7Z6V5bT0YuplOUS9Gfaezd5S3/Zm2x51fs7U0zP0vIE7RZphFtNxLdkycvqqq0YxBCR1R63WmrZsqXdti/pfL+lC9/6Ncb2zpkX7IwWN2sujZB2SNpT01zgDvDbGfw6Ea4GNgNcDi3P39VEz++kA608kEkOEkTzDTANmMa20ZHcgDKo/iDNKCHJ1PzezLc3s0SZ1XxlNlTe16MOHgdlmtjNBcu6jUVygXa4ENqsNhsAHgVPi9grAdDPbkiBt97W4/yTgM2a2I/B54Of9aBcASWMIYTF3EPRlb21+RsO5dfH1s84+u79dSCQSyxDr7a30Go4kk2x71LRkdyckYuyPlmxVk+zewDaSahJ9UwiDclv5OM3MJJ0BvE/SqYQ40g/Ew71kogG/IwgJTCak/jovexbo4/FbhYmSZsbta4HfkMtqLulnBE3axfHBIN/3uvj6E/+6w+hZAMAq919XL/PUplkeWJ/bceHYLMzUe4J678+ynI++TNU8jA39LjHDeT3URjHw4udW7y3ZKL5eHBhf5mGa73N3ieel9571THAesN486/NKTlqQCTV5NaB282GW3V/vqQuw1InOe0/XNR++vr797Lo71bf9Z+q9j/33pKEfbkVlTMP3J/scSnOgOtPpYudVnDftjovfZ4CJi+fUt/09bvgulnxPvJi6V/Hx3rBl3rOvvenEXG0bFbbRDsN19liFNGAWMxhasmUsJZvpe9kNEWZ5DWEhCllD2uVU4M+EGfJ5ZiW/jEFDtgt4sbb+OAAW5OuIikn1zLdm9imFrC/La501kUh0mOQlO/q4AhivxtyTA9KSbcIjwI5x2w/SfwM+IWlsbH8TSf3yrIiqRU8CRxMGzxpdrs33Av+MakQPSzogtqvo8NQJrgAmSPqE29f/oL1EIjHk6O21Sq/hSJphFhDNmPsBJ0j6EmFm9ghwDPCTqPU6nfa1ZIv4PnBuHJwvdvtPJmgh3xrXSf8LvGMA7ZwJrGZm97h98wjC60cDzxCE1yHMpH8R948FziZoxpZxtKQjam/MrDABZbyv7wB+JOmLhGuaR6P8XiENHqZLswnyGo9lS8FPr5OFv64148L69vPb7l3f9mbECQuzYPjF4ya7/S/Ut5+dskFDP7yZ1Jvk8gLvRWW6e7N+e8/G0mB9J6zgRbh7SoLcy0x2a91zWcP7l9bbrr7tRe39PZ4678n6tr83q7yQiUY8v3LmDbpkXPH1tJsPs8zMuWDcSg3v/XV7U7w3w3qR9TLKzN5d/jPx+TNd/7ypdvK8p7Nzl2TfsSUTsn6/MLnx38J/Xj7Hqzc/T3rmofr2s6/Yob69xqM3ZBW5nJZeoMGLEnhvWG+GvfaVdeVKAN665D4GSu8ySA4dH+iPATYHdimLBpD0JoLcaDdwspkdH/evT/hdW4Xgr/J+M2u5fpAGzBIGSUt2vYJ99wLbuF1Hx/29wJfjyzO7Ylt75Ha9hpC0OV/ucwX7Hgbe1KqNWPYYwhc3v79wccjM/gMcWKXuRCIx/FhGJtk7gXcCvyorIKkb+BnwRuBxQpL7i8zsbuA7wI/M7GxJvyQ4Wf6iVaPJJDsKkDSDMCj/rlXZRCKRGAjLIqzEzO4xs1bT4V2AB8zsoTh7PBt4e7TYvZ6QEQrgdKpa78wsvTr8Am4CZuZeW3e4jf8paOOCQbymrxS095XlcG8PG0plhmKfRnK/R/K1DdU+DdaLkOJvunu13R+CDvZOJcfeRTDD1t6/n5DGcFXCQFrb/wrgzkrtLc8bll7p1e6LEDc6ZMoMxT6N5H6P5Gsbqn1aXi/gHwTTa/71dldmmQ6YaQ0zkUgkEkMOM9trgFU8QaPq2tpx33PAVEljLITY1fa3JK1hJhKJRGIkcguwsaT1JY0jOBteZGFaeSVZSN0hwJ+qVJgGzMRw46QhVmZZtzfa+z2Sr21Zt1e1T0MOSftJepwQtXCxpFrSiZdJugQgzh4/TYhpvwc418zuilV8CficpAcIoSW/qdRutOEmEolEIpFoQpphJhKJRCJRgTRgJhKJRCJRgTRgJhKJRCJRgTRgJkYEkl5dZV+H2uqW9P3BqHs0Ee/j/y7vfiQSVUlOP4khi6T/AVY0sz/k9r+LkFz7MrfvVguJvSnbJ2k8Ib3YejgdZTM71pU5n+Axd6k1EcWUdKOZ7dqi/98xsy8V7ZP0UyhPrmlmn5X0RTP7bllZM/usq3cVgqbvq2PZfwLHmtlzkvroBefq+WGsYzMzu1fSDiXlbpV0gpkdIenPJX3aN3e9rwE2NrNTYxLzyRa0imvHbzazXZr1L+ZxLWrrQwVlVybkjZ3gyl1T5X67OlYDPkrf78mHXJlJwP8B65jZRyVtDGxqZn+JXpq/By40syz5Zvn1NavrjpJ+K3TJtnH1XAYcYGYvuntxtpn9T6s+JKqRhAsSQ5mvUqzxeBUhv+dlkl5FSHi9Wm5gWImQocDzJ4J4/QxgEcX8HPggISvNecCpVqxZeZuki4DzcLlQzex8V+aN9M3E8ua4r0oO0FpmmSplzwauIcs3ejAhOfhewIpx36bAzsBF8f3bgJtdHZ8jyJX9oKB+I+hvnhHft5xhS/oasFNs91RC5pvfEQb1GtdJOjH21d/HW12Zv7jtCcB+hHR1+fY+AhxOCESfCewK3BD7XbuHrwa2IEucfgBwd66qPxGSnv8DKEu9cSrhe1RLxvAE4bvwF4Ig+IHADyVdBZwFXGzl2TCa1bVPyTlFrFobLAHM7AVJqwNImkvjwKv4vjbwNqaDSRSSZpiJIYuk6Wa2U8mx281sG0mvA/YAPg780hWZC/zZzO5359xpZi0zvcSyU4CDCBq6/yZkevmdmS2Jx08tOM3M7EMx3+cngQ2AB93xFYHrzOx9VfrQDkXXJukOM9vavb8GeKuZzY3vVyT8kO+eO2+CmS1sta9Cn2YC2wO3mtn2cd/tuVnRlQWnmpm9vkm9XYTcrbvl9t9BeCC40cy2k7QZ8G0ze6crcyPwmhijR8w3e623FkiaaS0SqNe+m5Juc9c2y8y2dWUmER5KDiQMhpcCv/eWkap1VSEmWdjPzB6L79cl6EsXWgwS7ZNmmImhzEpOvqpO/JGbCGBmVwNXSzrNzB5tUd/1krY2szuaFYrmzfcRtCdvI+QSfQ1BEWSP2O4Hm1Txe8KP43HAkW7/XDN7PtfWaoQZ5xY0mhFf304Z4O+SDgTOje/fRQjY9qwB+FnO4rgvz/VA/ke2YV9cHz6GkER9DNlMxScQXWxmJsniOX0SoJvZngXtt2JjYPWC/QvNbKEkJI2P5uVNc2VWJlgfap/D5LjP8xdJbzGzS5r0YbGkicRZm6QNyVktzGw+YSZ7jkIC+tOBD9DX8tGyLkm7Aj8l5H8cF+uYl5sZfgX4p6SrCZ/HawkWA3J1bRuPAVxjZrc3uc6Ep4rgbHql1/J4AccTzFUruH2TCWuM38mVXQ34HnAJcEXtlStzN2GQuA+4HbgDuD1X5oJY7ihgrdyx6W57E+ByomgzIX3a0SXXsTqwTu2VO/Z3Qi6+e4DXAacUXFuVMnOBXmApsCRuz42vObHMVwiJwI+Jr5nAl10dawI7xna2JwyQOxAeEu7NtXcvwby8OkEpZRVglVyZzxPMkw8R1gRvAD6TK7MG2ZoxhIeCDxdc2xz3+hewf8F9vgCYGq/tGoJp9ZJcmQ8CjwKnEQawh4FDSu7lwvw9dGXeCFxNSIJ+JiHB/B4F1/YZ4Lp4D44Hti3o994Fde2Z/+4BGxEe4LrjdRxXUNeqBDPuPgQTbf744QQB82Pj6478Z5JeTX6TlncH0iu9yl6EmcvxwLOENZ4Z8UflO8CYXNkqg8q6Ra9cmT0r9u1qQr6929y+O3Nl3gbcT1ibezj+CN+VKzMj/r3d7bul3TJt3NMd44/m4cD2uWOHEDQ258a/tddFwDtzZW+q2N4bCQ8y3wfeWHD8UkKi9lnuM7/DHRe5h4yK7b4O2BcYW3BsTeDt8bXmAL6fqwBvzQ9OhIeDKwhrkT8BdutvXe749ILvwG3x72bx7w5Fr1w9t9P4ALoCuYfG9Cp/JZNsYshiwRR7pKSvE56uIaTlWRBNgte54quY2W8kHW6ZmfaWXH2P5sxR15rZrFyzN0k6mvAjfZj3WMyVm2RmN4dctHWW5sp8k+B48g8z217SngRTr2dJ/PsfSW8lOLNMa7eMQkcOBtY3s29IegVhhnxzY1XMBP5DXI6RtI7FNS8zOx04XdL+ZvZHCnAetFdK+h5wPs58aI3OOlhYr2tYs8uxqpmdK+moWH6ppLqjjZmZpIuBrUtryPp2hpm9P553dW0fwbTu6SY8eI0BNpG0iZldk6trX6C2tntV7fMv8CD+T/y7TryXtxLWK48DLrcmntaurcvN7A3AxQX7asxXEBCfKem7sd1aWGAVZ6161TQ6MvXEfYkKpAEzMWSR1E2Yfbwc+KuZ3SlpH0lfJqxhbu+KVxlUDic8/dc8WX8n6SQz+6krVvNYrDmUeI9Fz7Nxram27vQush/Pep8shHV0SeoysyslnZAr883oYPR/hDWqlYB8bGKVMj8nzGBfD3wDeAn4GcEJpnb9nwG+BjxN9kNpBHNyHTP7Y7yHW9K4ZnosfX+UvVNWw4+zpHcSrAGrx7aKPDLnxTXj2n3cleDJ7LlV0s5mdgvN2dK/id+fHXP7vgO8B7iLcL9q/b7GlTmecN/OjLsOl/RqMzuK4kGpRu36T4zvt4sPVAY8a2b/zvVlAjAJWDWGgNQGrpUI33nP+wkD/acJn/0riB7R8cGui7AkcB3NOZXwUHhBfP8OKgqPJ0gm2fQaui/COtPlhKf1KwghCfcC7ygouw8wBdiKYEacAeybK9PSHEVm+rrN7ZtV0N4GhLCD+YRB9Z/Aerky/yCsuf6UEFrwY+D6XJnVOnSvbm3Vb+ABcuuMJXX9EvgtwTv4a4R1rt/0o08PAJu3KLMjwVIwO/79F7BNrsy9hNn7gxSsPRPWm+fGMnPI1h2fI7fOR1i/Ht+iT7cDXe59d/570uL8KwteswjLBdu5cocTTPWL4t/aaxbw6X7c79taHO8iPAjuAHw2vrZvt53R/EozzMRQZifCj2dvfBp/CtjQzJ7LF7TMZDobKPO8rGKOaumxGNt7CNgren52WQzVyPF2guPI/xLMpVMIjhae6yQ9QvCmPN/MXqh3to1ge2BJnFHV+r0a2Qyqxr/pO3srYjcLITu3m9nXJf2AsNZYJ87WTyUMTL8m/AgfaWZ/d8WeNrN7aIKZzYihQZsSPov7LIbuOJoG3pvZccBxko6zMAtsxkOEeNCyONwaU8k8aafkD8bv4ycJ3tNGiNv8pZkttBLPX0k7EdY0d4/9/jHwY0mfsUYrR9G5D1Ms3uC9ki+XtD/he1RUtlfSzyyErtyaP55oTRowE0OZxRbXgCyECzxUNFgCSNoE+AWwhpltFd349zWzb7piReaoU3JVfQ34K/AKSWcSAt0Pde0UqubU1jItqubE7XmuyOlF55nZJpJ2IcTqfUXS3QR1lt/RXrD9TwheomtI+hYhrOToXJmHgKvimqBfd/xhrlwt3nK+pJcRZmpr5cp8yMx+rKDGtArBZHgGIbylFvc4XdI5wIW59uriDpJuJ4gunGNmPmYVV/7RWHZ1nIm4oNxRZWuPjvmEdcDLc33yDx/HEYQpriQM4rsTZrGe3xIeFmoD3Xvj9R/QpH/TJU2uvXf36Ym4bQQHt5kFD2De9D0htpNf6/4YYT1zqaSFFJvAmw6qieYk4YLEkEXSfIJZD8I//4bxfZEs2NXAF4BfWRb8XRTMvwNhVgDB6ee2gnZXITjriBAE/6w79rVmfY4zsn+a2WtUoq5iJaoqklYFfggcbGbdbn/LYPu4fzPgDbGdy/Ozu7K+m9nXc+X+H2EgeANhHdSAX5vZV12ZmnDEjwkD0wWKgfcqFnVwzTVIzK1LWFN8D2FGfA4h0e9jrsy+hLXDlwHPELyb7zGz/JrlcQTP5dra40EEb+IvuzKHlHSq4YFG0lpk6783m9lTueN3m9kWrfbljq9BCHPZMb4vuk/TCGvKHzazK8rqiufPqNVVlfidXIFgvi4bVBMlpAEzMWSJP6almBMqkHSLme2sRrWUmeYUW7wXZX5fgfdjvq3KJixJ61prEYVa2ZUIUm8HEh4ILiAMGDNcmfuAV1kUPYgOIjea2aa5uprqtlbsTxewq5ldH9+PByaY2excuVMJjinrA9sS1vmuavcHPFfnxsD/o+8DwyyCM02Dt7GZfTh3/u2ENcLe+L6bsK7X4NRUoR95D9U++yT9DjjRzG6M718JfMrMPlBiSp9GWD883Mz+3KL9dQnfgVe6ff772UWYcX7CGpWFWvY7MTCSSTYxZHGmuPXJPCDvjuuHeap4rTbzoqzi/ejP/S4hbGQBwYS7DfC/0ZR6AVEVR9IfzWx/yplFMFkea2Y3lJQ5nr4mwmNy/fkaLXRb4yD6Rfp6v77ebfdK+hnRA9nMFlG83vdhYDvgITObH2flDepHkk4nDBAvxvcrAz+wnGh6bpbZE/voqeJtXGMqzdceNyaYXPOqSRuogteqMjH0sQTlqMfi+3UJzknQV/vXCGbtz5nZMyX9zgqH8Kexud3++7mUIG7w7tinyt62aVAdGGnATAxZ4uzrZMJAMDPu3k5BM/PDZjbHFf8UcBKwmaQnCN6GB8d6jgK+DEyUVDtHBNWfkyCTaFOJjmpB9/Y2sy9K2o/w4/VOQmjC72h0JNqg4FzPBq3WkuKM8VKgNuP4Ut5ESJil1p05zOxJBa1Yz5kEk+c+BO3dQwjxiHlK17kUM5oQBkuADdQYi+rZxvqKgftQICTdRBh8ziNk2ih6GHoxrv1dC5wp6RmcULujaO3xyFyZUwnr1D8iOId9kCye8WPAEQTTr7cozCELFWkphl5g3h1L8N6uhIKcX15mr5mEoO/3DLLvX73f7QyqiXKSSTYxZJF0GmEwOtaZ2UQw221kZh8oOKfUa1UVvChVIU1Y3HenBeeik4E/mNlfFQWzffmic3P1tJz1xXIvJ9NtrZXxsYM3m9kutfbifbght847w8x2lBNAr5myc23V1rl6CDPo+jqXQtzqYaogmh5NqXtY9PyVNA242hoF4Te14mwwvj8rkK231byNz7QCB7AKa4+1e1AXps+vBaqJ16qklcxsTryWohvwvKRfAj81s7sU4mdvINzLacDnzeysWFdRirRpBAer93mLg6qlpmvW78PJBlWf6WUOYX36xKLzEo2kGWZiKPNqMzvU74gznmMl3Q8g6W2EGLnamuH/AftLepRgDvRreH+RtIKZzZP0PoLZ9MfRBLYm4Ul7YpwF+SfwSQV9+4ukewkDyifiwFebmW4bZ7Ki76w272DRctanCsH2wLmSfgVMlfRR4EOEcA9PFVUhzCw/M/XHDot/q4im/wC4QSFNmgieu9/KlXlK0g/JPFuvJjwg1ddM4+e1JsGh53ngbyWD5R8JQfh/sXKFnUVxnfZ+SZ8mxNBOjufnvVYbsODd+3vCZzWDLD1WvQjBovBaM/t43PdB4F9m9o54DZcSYnKhb4q0mun2fuubCuxPtE5N1ytpas4EfpCZ/dzaCGFJlJNmmIkhi6T7zWzjkmMPmNlGCo4eu8Z1tH0IXqYHEcyTB5hLnhvLbktYbzyNYO59t5m9TsF78lCC+devQc0FTrPGPJe1+qYREln3xFnQigWm0mbXtzLBkaXprE/B6WebuJ7YrL43EoS8RRhU8mmk9iGYNV9Bphh0TN4JJc7im8rsqUkcYq6uLcjWf68ws7tzx/9IEAOvmTHfTxAo9ym5PkLIjXpFvLbXEQbVU3J17UUYoHYlmHhPzc9eJe1MEBCYSlBEmgJ818xuVBvevc1Qo+PZxcB5ZnZa/lhVJN1A+G41NeuqIC1Zvr34Pf1fWks/JoqwIaCekF7pVfQi/Ih+lfhg5/b/P+CMuD3L7T+FsL5Xe39r7ryaGs5XiRkxCsr0yYJR0rcZhAFj5QFc360Eb1cIqbjeShjoH8yVu5Tg8ToY9/iIgn2/IIST3BPfr0xfQfhzCbO5PePr14SBwZfZkKiqQ8h48llgaq7MzIL2Z+be34dTKCLEfd7X5JqmEGbr/yakJfsgBSLsA7xvl5ftIyj77BM/yxeJAu8Ei969/WjrNsJa+9Ytyt3h/1cInst5sf9zCEsAtSw7k4o+g/QqfiWTbGIo8xnCj/IDCsmIITib3Ebw0oQwIZpMCEh/A0FTtUbeWWdudAB6H7B7NM3lvRH/Ium9NFkriryH8EN8i6TpBGeSv1v8FaqIqKYTWxpsr76xnp5FBDm5r5jZ5SVlPgeckNv3SgvroLfFdl5QEP72bGWNMYdXKogueP4I7CRpI0Kar4sIJs23uDILJL3GzP4JoCCqvyBXz3OEmX6NmuxdH1Sey/R7kq4vOide476S3mdmv1OJOIWZ/bCi88zHCEISaxEeSGpWhwaB9TaweA2HKij+LIK+scgEb+1zomm+1o+/5ura0MzeI+mgeE3zo0UhUYE0YCaGLBa8YA9QCBep/TjfbY2KMCcQVG+eJcyIpgPEdch8WMl7CIosHzazpyStQ0g95amyVoSZPUBQ5vl/hNnEKUBPNOv92HKJosurqSTpd1F8FVVQut6oEDazFcGz9DXmPFZ9sYJ9VWT2bpW0qzXGIebDKXotZB95JyFm8ae1QdjxCUKGlCmxL88T1nFxA9cDBIWmP8U+vZ2g95q/3gsIYTVnAG8zs9rnf040VzcLHYLg6ARQek+p4JFKCFU62HLrrGb2N/om9a7KmyuU+VLs3yfi+8sIyw6eStKPiWLSGmZi2KMQG/cBgnm25k27FsEM91h8v6WZ3ZU777XAgWb2KbevjzpQk3a3Icwy30L4IazNZt5vubWkgnN/ShjAzy46bo1SbQNC0seBw6zAW1fSY2a2Tm7fwbFvOxDM4u8iZMI4T41xiJsCDXGIftapEDJyAiFx9dvM7OGy+6sQQgQhXORAMztTFVSVcnXsaWZF3ru+zI7mRCHivn2sjTW8+DDxZTP7RsnxLxH0b8cSkgdcSvDY7dePbW5NtEEe0JwiUjw+kbA+Weh5HB8cjiY8gP6dKP1oZlf1p2+jjTRgJoY9VRwplIVbbE+YZR5AiNX8ozmXekknEUIC7mhR3wzC+tRvYh2L3LHzzTmtlJx/CEGI/avA1wmxgXXMxfKpmvB2s7bmEsyI+dhFARPNrI+lSSUye2pPfWkLwlriDWZ2loIAxbvN7DtxgPwUwYz5J0Jml08RTNO3m9nbq1xbrs9jCbMr73H7S3Ni7pJuBT5gZnfG9wcRzKZeVWd9wnLAejSa5fd1Zap851YE9gLeRPDwvYdgIv2bmT2dK7suQaXpH3HQG2MxNErSVgTv26bygAoSgt8DxpnZ+pK2IzhH+X6vGjdfCX2lHxPNSQNmYtij1rGOmxB+PGcTTLfnEOLh+vz4x3W4jcjSLhXp1nYRMnN8u6S9whi9GpZJ3E2zELfX9Mc3rsvVqAtvm9N2bUWrexTLrBzXKzcEHjezRZL2IHgV/9abdKM5uw9uRt8dzzm4pK0/AS8QYhTfQJYz83Azm5kreyXFDwz5WNWTCbM673HbY2YfcWU2AP5AeGh6LcEysY+5MBaF+NHfEJxo6qZoi0mpY5nvx75XFjGPDxBvJoheeO/tjxISQE8zsw2j5+ovrVGKr6U8YHyIez1BorA2I73DzLZWCL86haAS1AO8x1rnzkzkKfMGSq/0Gi4vcp6uBcd7CY4iG7l9D5WUXbfoVVBuepP2HiZkBnmY8OP0LMFJpQd4uN3+l7Qxo5P3yJchqCqNITw43EeYtVySK3sHWW7K+wk/xHmPzH8SZjtFbd3htrsJs6YJJWV3dK9XE0KHvltQrihvadG+TQjr3n8lzLDzx2+qcK/mxu/VErIcnHPisbsJZs8NK342M4FxNOYyvSNXppandRYxV2f+2sg8rn09t9f+ApvF7VcSBCSW+//ucHslp5/EkCZ68K1tuWz1OfJB3nneSXi6vlLSXwnrhoWegRZEDPqImBcU/YekzxNmq/Pc+c+b2fqx778GLjCzS+L7NxNSirWFioW32/3freIJWSvT0lnHnFqP6+Mnc/U9RMj3eRGN9+iHZCIKWIhjfdxyMZzu+Izcrusk+ZjQMRYyufRI2tCiU1icTfbE7draa41phIH6JklYo7fpj+P66d9p9Eq+1W03cww6iCCm/3dJzxGECs4xsydLyi8ys8U1Z1VJY+g7o35RwRv8GsrlAe9S8PDujrPUzxLCagCWWpA0xMxuUl/ZxEQFkkk2MeSRkzErOd5yUFVIkfUGgoflQQTT1W8JA9rfXbmvEUXMLeSqfBkhvvDVufoeLmjGzK0rFvXbmch8OMgkQugI0FcNSI0ydEsJM9cfmNl97Zp/m5V167yVnXWKrs29L3TasZACrYfsB1/ARMI9KLp+f41dhJnmTyxma3H9fgMhvOehWM+6wActiLW3s/Z6HMGc+yBOWckaZf9aijvEcrsSHKj2j/X93sx+nSvzXcJ6+AcIa6efJHiDf8WVaSkPKGkS4TOri1cA37CQS/Zxwsy8xuf8e+ubEzVRQBowE0MehawXJ5rZLU3KNB1UC8qvTFgLfI/FtaK470qiiLll60B1FZ42+/03ggLO7+Kug4Hdza1fVeznHDPrKTlecwgSsA5hXVAEJZvHarPdim3VBp5SZx1X1scq1gaxaUXXFmdGmNlLVfvizl2ZIPBQu8baA8OxlsVu3uY+q/EE710I4gaLcvXtSjAd1xxqVgI2N7ObXJkHgC2srzydr+cXhMH09Wa2eezn3y2ny+vK70EQfN/CzMbnjnUR4or9QHeydfDHuezhpYblPI4TxaQBMzHkUdBs3Qh4lDArKXLEaTmoVmjnVoLpqqmIuSu/G309KX/rjk8jeL/WvDavAb7eaqZX0KepBBGAUyyXFNqVKzT/mtnH2mirPvBUKOt/gGvppv7ozaoK3p1nkOnVPkvwUG0I72nRThVnpfzsqQE/e4qm5R1qg1EcrKb7NiRdSAjDKU3F5b4ffrCeZY35KXcmWDP2JwzyZxOsFUU6uOOAzQgPBvfVBmuVJCEn+x9YScUi7v769y07VtCPo8zsuKrlRxtpDTMxHKgyI3slcLCC6HrhoFoBUU3EHElnEKTfZhLXyQg/WvUBMw6Mh7fRflmftiWsif0m/sCfApxtjenNdjWzj7q2L42mvspeuwSTNWqSM9Jt12cksU+TC9YgTyLkgLwyltuDcC93a3nV1Aecse79BwiDz6MEDdxav7sJ68yV1mn9zM1C/s/87+BU4F5Jt9C4hukHnlJxB0nfJphhnycMkq82s8ebXOdbgV8STLYC1pf0MTO7lBDHuSZwPuEzf6ygiryI+0A4gPDZJwpIA2ZiyGNZIumGoO0clc2czZuy7ysEd88hmPa+ajkR88hOBPNa6ZO9QjjL5+k7C3192TklfZpLGGh+Lel1BHm5H0n6A2GN6gHgSUlH02j+rTmZ+Mwafcy2wPqxodoAdCrlOSNr1/Z7gtm2B7gFWEnSj83MKyetYE5IwMyuijP2qvyKbBDanZBI+zMEecSTCIIKAP+xvtKFZTwk6bMEvVwI64X5HJxNzZeRnxASha8u6VuxL0fHYwuBN5nZ/fmTJL3a+oZz/ADYM36OKIT1XAxcaiHLyRSC49qvFaT5ziEMnrXP67bcw5NvrzD8pwlJJq8ZNgRcddMrvZq9gH0JoQvzCKatXnIhDK7s6oRBYR2C4kk77eSF2FeFRuF3d+w8gpNHs/pmEQLpd8GFRrTbJ8IMal/CD/RtBIeNNQg/0v+K5aYBP47Hb4vb03J1/Rp4i3v/ZuBXBW3OiH/vyO9z72fGvwcTfvDHEkMYXJkLCEL568XX0QSzcdVrn0UW6vIzwqyyof24fVvF+laO34+zCWEsTxMePlbv5/dyM4LYwqcJ66C1/WMIptjPEzR3IcgnXl/UV/oK2yu/L+7vIsSPPkuYuff53pIThc9/p9v9H0ivxleaYSaGA98gpGxqCNr2BRRUTvoooRASM7ckOoNsLOn82N4ZhAGzS9IHzOyvsVxtvWhF4G6F8IYys91SM/sFA0OEh4Urge+ZmRcQ/0OceWHVzL+lZtscpTkjHWMVlHXeQVg7XqK+Gt4fIqgYnU+WAqxSiqxIN5mX6hsIwf01/G/XG6jG5RbWKg9sVih+F34KbE6Ij+wG5lmj5+5PCLO8nxVUcTIhhdrNwE8kPUmwSBxpZhcWlJ8u6RJCBhgjmEVvUZaT8ynCAPxaQmzrfmZ2re+y286b39udMaYZZhPSgJkYDiwxs+ckdUnqshAmcEKuTOmgWnEN70RCPswuQt7FN1vIkbgZIY6ulvWhnfWiP0v6JGGm5QfV59tcV1xsJR6mFjVnK5p/m5ltPYcTQl0+S7ivrycKojt+RXD0mQVcE8M2Zse+TCCYazciCBv8nzl5ulimyvWfBeyroAq0gDDgopD9ZHaubBWkII7fx4xujbkuTyQMqucRBroPEMQOPDOAoyVtSvh8z7Yo/A/sTEjF1RvvxVMEEYPCDCuEZYanCXk+ISQQnwi8jWBFeJAwKz6M4GBVj821EBvqryd/be16dZ7XZvlRRfKSTQx5JP2DMJM5jjDrewbY2cx2c2Wmm9lOChJi28cfq1lmtm2V0Au55LuS7jGzzV3dt1nmCbkRsIbl1qEUxA7+Yy6TiprEalbs009p7v1YF2iP1/1Lwg95jyszw5UZsNeuq2t9M3vYvRdBSel+SecQhAmuJZh9HzGzI3LnVwqHibO9tQghG/Pivk0ITka3xvcrm9kLFfp8K/Att2sCsB/wZO5e1r5LPql3/TuQq3MawRHpQMISwMZ5z95Wnr6SVikbTCVdRfYdqN2vGmZmr3dewiKkhqt5BYugk/sKV98mhPXbNcxsK4UEAvua2TfL+pfISDPMxJClNjgRxAYWEH4MDiaYWz+TK16qhGLVlHd8+qp8PkY/aJ0AHFXQ3dnx2NvqJzWJgazYJ58uq49Ae46W5t9WZlsFRZ5m53tz8x8J2Uxqx0zS2YR12i0sxsRK+g3BNJmvq5IaksX0Yblz/5XbdbnvS4tr+KN/L+ksgpnTM18hzGNmNFn/h5zTk2MjwlpmbQkAYDNJtfRjAjaM78s8t29UyPd6KsHRx3vx7lHhsn5N8KR+KG57FZ98eq9fA18gWAgws9ujA1caMCuQZpiJIYukvwBHWS5ziKStgW+b2dvcoDqTMNB1kQ2qF+dmWM2Ud2rKM151hvh+gpmNjeVvsfLg9KL6t6JveMZvW5xTtK9whuOOH0OYefcx/7oyTc22kv4L/JtgCr2J3HqWmV0dTdRbAt8l/PDWWAn4gplt2c4Mq+r1N6PVvWlWLppULzazjdy+dQn3cizhIW0K8HOLXqyxzHcJs9MHCV6rF1gUp1cbqkKxvAhZTT5EMOeeC5xW8GDQ7NqqxKseBbzTzHbOWU3q1pVEc9IMMzGUWSM/WAKY2R2S1otvTyAMqjWZtV5CQuKtgW/jZnw0WcMzs+6KfWq29jbRv1EI7t+DMGBeQjBP/hMXq9msTzlaPdnW1hj9IGaEtFA1ziOYbU/GmW0dawJvJDiYvJcQ2nCWNQoNbErw+JxK472dC9QciraVVAtzEDAxvu8je0f1629GV7M1Ub8erL5CAE8REi/78rUBbQFhZl/Eg8CrrCA1Vn5ALEPSDWb2qjijvAy4LK69/w74ZDSzH2lmN1SprkKZA4CnFcJWavGj76JvovVEGTYEXHXTK72KXsD9TY49EP/2cb93ZfIZH1qGXlTo0/PARwv2f4QgsN3QPmHGOyu+XwO4rD99ogPu/rSR4QQYT3CC+i/w6YLjr+pAf1bu0GeyiDaywzT7vhCyehS+Csq/nCDCsHvt1Wa/74x/VyGYyqcTHlLeSZjM7FS1/1W+H/H+bkDIPTqf4P38Twqy8aRX8SvNMBNDmemSPmp9xao/QnBugTDTKaNhxmedUd55AvigpINdH3YihB/slyu7wILz0VIFzdJnCOEGlfqUmw1Nys3azBpnai3NvzTx2nV1jAfeSphlrkcWoF87/kUz+y7wXoXkyw2Yc56pQC3MY6Cfyd0WPKPbyg4TZ1rvBQ60kIh5n9ohwsD1libnHk9w9LmbRqWna9rod232fwMhjOkd1qgINF3SL9uorxVmZg8BeykISHRZ1NRNVCMNmImhzBHABS0GpyqDam1fJ5R3lpjZbtF0VsvecbGZXZFra+XYt6kER4sZwEuEH8dKfbLmKaTybX2W1ubfpmZbSb+N13QJwXv2zoLmao4t0wuOtYuaXX8zM2ss0yDpR4U4U4XsMwcSHgi2JnheHxjL+4wli6y5aXU/QkabRU3KVGVTi1PAPGb2HYWY2F2tMQY3zyMV2pFCMvKvAa8BTNI/CUL2ZSEvCUdy+kkMeXKD011+cJK0BmEGtJiCQdXMnnJlW4ZeVOhLS+eKWjng7RZTjsU115XM7PZcuY70ieCgsi1BSWbbeF9+Z2ZvbKOeXrKUW/6HoXBGO1Biv7spuX61mYlFTbLDSDqMMEi+nOBUcy7wp3wdvm/NPmdJlwIHWD8ysLg6lpBJ9PXBGkNdbrOKwvhN2vsyQerwGhrv0R5mttdA6h4tpBlmYshjQY/0ypJjTwMtZ3yRTinvVC13CWEWg5k9UlKuU31qaf6F5mZbMysLnfDnX0QYnJcUHbc2MmNESq/f2k/EfRBh9lQzIV8T90EQI7gBeK9FgQFJDbMFNSbqnihpe9znbS6BNGENcKaky2k0bzeYpKPH7MZm9g9JE4Exzgz6X3JWkCZcLml/4Pyy2ahaxFia2bclvdfMvuFO+6ak91Tsw6gnzTATo4ZmoRdVzX+qkIg5lruV4ETSKo9naZ9atZFr60bgywTz4v8RzL8zzeyDrlyh166ZvStfZ5O2/gusQBiYCkNP2qjrNuBPtA6H6UToySoEL9GDCN7A5wKHWmNQf+FDWdalhgTSeeWjWqHTXZmPEtR5ppnZhgpZYH5pWf7VStaKWHYu4b4vJUsk3TDrl3Q1McbSspCRhsTfkn5IiIs9N+56F7CLmX2+Sj9GO2nATIwaNEDlnTbbupUgL9cqj2dpn9psq4r59w4GbrbtBu4DrgO2oSD0pJ2HD4pnWA3X38zUmutbpTVqSWsT0m8dRBiELjCzLzfrc+78N5rZZQriBjXJvPusr/zfTILw/k1uAKsP9JJuNLNdK7S3pVXIIaoYI6wmMZZu4K2Zv7txZvhOm91HGskkmxg1NBv0+mH+a4WokHKs3YG4SVtVzL+VzLbNMLMeSXPM7JDoUXsQcJWkr5vZibFYO+nEqlx/M1Orp1Wcae0aHicI9f8gDrJ1MfbaYNiiP9+J64+nE5xtBLxC0iFm5r1kF5nZYkVReoW8m17Fp+VgGTkD2CE6d21Mozndt/esWsRYWkVHskQxacBMjCqareFFmnpatuO56WZSk2Kbj5rZf6v2qU0v0R9J2rmZ+ZcKXrsVkUImjcLQk3YfPlp9JlY9HKjt9WALajo+l+Z3CCICzRBhwN3bzO6D+uz2LII0YI2ro6PNRIUcq58E/txO/2rtKXh9Hw6sTVC12pXw2fnZ86cIeUI3k/QEIS714FxFfwR+A/zVzLwcZKICySSbGDVUWcNrZf6rYrpVSDX2E4LIwdGEXI5PEwaXL+XWuUr71I6ZWNK9NDH/Kkxz1m5ltq1wD39LWPf6ISFDR1HoSa1sy7XHip9JVVPrMQx8PbhuzmxS5laC8842uf23W6O5vQv4MLA34fP4G3BymdNOi/bGEmTzbjSz7RQkCr9tZu8sKF8aYylpL0JC8F0JM/JTa4N+ojVpwEyMGqqs4aliRo+y2ZOZfUwhVOQAggbplcA2ZvaQpNUJwfp+wKjSp9K2XJlC/VJrjC1s6ihTZUarNkJPqqw9Vrz+SqE3nVoPttaarLcSZnm97treRxikPpQrO44gzm6Edc7FVfuSa68nrk/OBF5pZosk3WVBcKFWriHGkhCHWxhjKWkKwULwFYJ+8K8J973Q+zkRSCbZxGhiQMo7OZqZbnujqQ9JD1tQV8HMnpG0tN0+tWirtu/R2F4z8++tLcy2LdcerULoSezHylRbe6xy/ZVMrR1aD67CI8AnCCbQWhjJNeRiKiW9lTDQP0i4j+tL+piZXerKNMz8S1gM/Cea0y8kaM6+QLAmeM6O/dg/vj+YIAzfEGMZB9b3Ae8nyOWdSRhkDyHM9hMlpAEzMZoYkPJOrq5mouFdccDoAnrjdi38Ij/gVFlXLG2rmflXUoP5F3glcLCkQrNthx2fqsreVbn+lpJ+NSqsUbfikVjPbvT9DvxW0mrA0RYUfn4YX0jakpCxxT+k/ADY02KWk+iQczFwqavTJNUdtorIOQcdoxD+MoUsqXmNtawkxlLSaWZ2qKQLCAL6ZwBvM7OaU9A5kjqh3jSiSSbZxKig6hpeG+a/UtOtpEcI5roikYO6ibCNPjVrqx3zb0uzbSzXkZRbhNCNZunEql5/JVNrlfXQWK5wMHTHzwA2JJhde7Ii9lmFnJ8/t0bvVCS9FviEmb3X7WtIBRev92bLpYeTdDot4nVjudcQRBBOjQP3ZGtM4l0aY1kzNUva04IQSKIfpAEzMWqo8qMvaYaZ7disTAf7syXBcabyQFRQR91JpcChptCBpYXZtnLcY4t+NZW9c+XaGohbtFllPbR0MHRl7iEkwe7z4yhpupntVNJ+XiTgF4S8rOcSTN0HEEzb/4iNnh/LNXXYimW+RpB83NTMNlHQxD3PzF7tyjSLsZwUzy9UqrJGFaNECckkmxhNtFrDg4rmvzZMt804o0qfWrTV0vzbptkWqsc9tqLK2mOVz6SqqbXKeuhOlAyGjjsJakBFeSKbxTGOzb2fQLjPr4vv/0vIoPM2wgB6ftxf5UFkP2B74FYAM3tSUkNfrEmMZRxMf0CJ1YPG8JRECWnATIwmmq7hRZpm9HBUCpJvgSr2qVlbUwgzuNoPoZ8p1AaFbxBCGwrNtoQA/OykzqRBE3BRhYePltdfZmqlMRMLVFsPbTYY1lgVuFvSzbl+7ws8IOkttfVd18c3E3Jyej5f5KGaxzlsrY57IMixOK531kQJVsgXUPMYywfafJhLFJBMsolRQ9U1vIp1Ddh0G82W+xUds8ZwkAG1FdcT32dmd1Ux2zab0arzsneVwmFobWqtuh56JbAdYa0vPxjWyryOAszsagU92IuB62nMjvMqYB+L3tGxnvsJpt9TgUvLZrXRAvAD4GWEWfG6wD3WGDLyeYLKzxsJKck+BPzezH7qypTGWJaZ5xPtkQbMxKijwhpeS/OfOiSabjHmr1mfBtpWdAyCYHa7gjBbq81IrzSzbQvKdyTlVlVaXP/NZraLpBnAnsBcwoCyWa5clTXq0sEwV24NglAABEedZ9yx8YTE0/WUc4TBa2GuDhFCOj4U6zoXOM0PqrHcLMJn8w8LibD3JDzgfDhX7o04EQQrkfBTcYzlU+bCWcqQ9Ecz279VuVGLmaVXeo3oF7AvIVzgVuAtBMmwG4GngENyZb9GMFs+TZgZPAX8oaDOhwteD7njAl7Rok+LKvapaVsVrv+R2FaleoAZFer8NfAW9/7NhCwZ+XJbAe8GPlB79eMz+TlhQP44cD8hdvDUgrZOB3au0Pc1gH3ia/WC4+8mOOCcTjD7Pgy8q83v3A2593sCTwAvAlcDr3LHpse/swjiBwCz+vldX4VgTp8OXETwVP4pcFXF82/rxP/cSH0t9w6kV3oN9iv+EG1CeMp/Cdgg7l8duCNX9g6Cs8ys+H4N4LJ+tntHJ/rUoXtwa4UyW8a/xxB0T9cCptVera6t4F6WPnxUvX5yDx4EM/E2Jf2/l5D+6kHg9vhZ3p4r03IwjH1b3b1frZ0BjDDzvi03eF0MvJNg4t4JeNiV/wcwOQ5sZwE/Bq6Pxz4MfMGVfRyYQ5hlfzzuOy3+vQC4GziKEJPp+zS9U9+T0fxKTj+J0UCnlXeIdbUy3TbzAG2nT50IyK/CGcAOVHN8aibcUONdZGuPH6ytPcZjla7frDGw38ozsUA1b9OvEGahz8S2VyMMWH9wZbrMmWCB5+grONGMCwn36wbCPX2HhQwpNaZL+qWkjQgPZG8HFgD/S7iP6wKfiWU/DrzJnftfM1tb0gSCNu0vCWnWAH5iJTGWVhIKk2iPNGAmRgOdVt6p6rnZzAO0cp9atZV3eCmhioapoLLE3EBl79r5TCqFnlg1ecAqg+FfYyzqWfH9ewj3vSoiDJibWpy2FfT1O5L+AhxlZrVYyV7gdElbA98mhJ/IGj1tz4vnL5Q0Me6bJGl7YLakPjq41l6MZWGcZiKQnH4SIx51WHknHqviuVnqAVq1T220NWABgJwT0oBntJJ+DnyZkG/y/wgPHzPjbPMRql9/q0ws7WSH+R5hRuYHw9vN7Eu5vu8P1EQBrjWzC6iIpNqAfHbRcYsiCcopAeXquMPMtpb0gJltVHC8ixAqsoFCjOUtlN/LyuEkkvY2s79XLT/aSDPMxIjHzNarWHQLwo9cFfNfFSH30vi6qn1SUAOqYiauNAur2GbL2XOz0JN4XMBxZvYi8EtJf8U9fLR5/a1MrZXjTM3sC7nB8KSiwdDM/gj8sUXf1iVI1f0jzvbGWEiptQD4FcHJpxlTmxyrzR7/LumbZnZ07vixQG1gaxljGR+6imZHeT3hNFg2IQ2YiURGJeWdSBUh98L4OmBLqnMGcGMFM3FTAYA2zbbN1h5rNBVuaHPtsRlnWOvQm7bWg8sGQ0n/NLPXxBmbH1yKUpd9FDiM4BC1ISGx8y8JybyfM7NvV7i26ZI+ama/zvXjI2Qxnl8ATpb0AMEZCcJnMx34SIU2auzTRtlEGVU8g9IrvUbDi+DZWMXTspLnJuEHbhWiqz4htOA3/ehTlbbWLXrlylTyviXEHUL40V4pXu+9uTJVQk8qhXk0Ob9S6E28zyvHe13brnn31ryd/xn/ziV4mdZec4E5/ejbTGAcLgyjdn8JSZ6r1LE7QQDhKsKD1Q8IISc3AGvmym5AWNN8G7Bh7tjeRA/nFu390X1X9orbE4EVB/J/M5peaYaZSGQYFTwtzSrPnpaY2XOSuiR1mdmVkk7oR59atmXV5NU6NnummuZuFdm/ZnwDeAD4GM1NrVNoIQ9oZq+Jf5tpwQIg6Qwze3+LfYvMbHGYuIOkMa4tn46rGSdYzCBCJoJwsZldkS9oYdacl96rHfu7gmpU08TXwAYtZsaJFqQBM5FwWDVPS6g2+LwoaTLBg/TM6Awyr0n5MqoItFcx/1bRbW269uioEnpSObtJCb2EgemWFqEn61WpLK6HHllhMNwyd3wMkJcmvFrSl4GJCgo8nwT+XPG66lUDWAgFKQwHabeuFhgh6fUuwE2x7fvjA0iiAmnATIwKWq3hxQFny/ikXiWjR+ngo2rxde2sK1aZqX2DoCHaIK+Wq6tjs2erEHrS6uGjwvV3AYslrULr0JMqnJHf4QdDSUcRvHonSppTK0L4DE7KnXokQVTgDsIM+BLCem47dDJEoWpdpTPjRGvSgJkYFeQHggK+EY+19LSMNBt8TqB1fF2VPmFmu5aFp+Roaf6taLaFAabcahbm4R8+Klz/FMK9m17rl7+cZn0r6OtRBGcZKxsMzew44DhJx5nZUc3qs+C1fDphpmbAfWY21Ace0ZmZ8aglDZiJ0UTHlHdazJ7WMLM7Cs65QyG2s2qfqrRVo6X5tw2v3YGm3GonnVjp9bdjajWzu5qVMbPjJB1AEC1vNRgeFWezG9P4MHCNa/OthLW/Bwn3Z31JH7MocN6mV3JTOljXlwiqRgOdGY9elrWXUXql1/J60cQDlgqelrFcS9Fw4P4mfXigjT5VaWsjQkzhCgQz5RjC+uJXgR1zbVXy2qWixy0lmrsUeI6697dVvf42PtdK+qe1cvGz3YXgpbo7sHuu3EdiP14gDPgLgCsK+r2Re78hfT2JO6YJ3Kyu2j0reLV9L9Or+SvNMBOjiWZm1JaelpEqs6cq8XVV+lSlrROoYP6NVPLatWoz2k7J3g3UMSh2tdosLH4GhxO8Q2cS1n1vIKTXqnE4QRT+RjPbU9JmhHvpmWtmD7j3DxFCVDwdE5NoUVflGEtl6dkaMKeslCgnDZiJUYN1QHkHGGetTbdHABdIOpjGJMPjyCWMbtYnqpmJ2zH/NjXbVl17jDQLPan68NHq+qtiZpXXg++g9WC40IJWK5LGm9m9kjbNlZke2zs3XtMBwC2S3hnbOp+Bh9V4SuuyxmTbfdSHcvV4EfYJsd9Nk4InMtKAmRg1tLGG14x1W82ezOxpYDdViK9r0acqM7WpTfo6MbZRyWuXimuPcTbXKdm7DZtcf7tUmdFVGQwfjw8DFwKXSXqBoGPrmUB4mHhdfP9fwv1+G2EAPZ/OzJ5rtKxLFWIsrVHIHeAEhcTcX+1cV0cuacBMjCaqhF60opvqs6cq8XXN+lRlplbF/HsC1cy2g5FyqxlnEO5n6WfSpsNLlRldy8HQzGpWgGMkXUn4HP6aa/PzBYNPAx2aPbdTV8sYSzVmM+kizDjTOFCRdKMSo4lOKO/cZVHbtBlVPDdb9aniTO0IQjqqZubfqmbbjqfcaoFo8ZlUNbXGzSpxpi0HQ0m7Ej7nuWZ2dVyj3Z44EEVulDSTkBj7UjPrsy7YIYtGO3VVibH8gdteSnAke3e7/RmtpAEzMZrolPJOFWrJmAe1T2b2tCQDvk65+Xdqkyomuu3Ka490Zn3OqHb97ebDLJ2FVRwMf0HjZ/dSwb5NgL2ADwE/kXQucFpthh7phEWjnbquVkmMpaRdzexGM9uzn+0nIIWVpNfIf1Ex9IKcqHpJXVWFtW/rRJ861NZZwEcL9n8EOKcf93NLKoSeVLj++6pcPxVDTwhhOPcTBtyHCabnu/L3CkIe4Pi+i1xYCiFnZ77u0vAMQnjOE8CLBPH0V8X90+PfWYTE1eBClNq85y3ritfyUUImmT/E7VrO41tduRv604f0SmElidHBCXRQeadim61UXyr1qUNtHUFFr92KtEy5VWHt8QSCA06V66/qPFNlFiaLowbUFXvyv4MPSfosYVYJYab2ULyudczsMQW5vvcB7yc4/3wGuAjYjjBgrU9nLRot6zKzXoLX8q8LzvdaswNaTx3N9EePMZEYbpSu4RHSZXlulbTzEOvTgDCzp81sN4LZ9pH4+rqZvcrMnmqnLjnNXUlvAe4CTgTukHSIa9MIKjJlrGFm2xb0tc/1m9mjFsytCwgPB7VXniUWHHHq66E0hlFAHAwljY2vw+mbBeTjwG6EWeMTBPPzYfHYhfHvDYTUZ+8ws7ea2flmttTMpgPnSXo1wSt5PsEr+a/AczR6JbdE0kZV65L0sKSH8q94uEvSynGgr21Pq73a6dNoJs0wE6OBqU2OTcy9r5rRY6BSZZX61KG2gI5lxaiacguarz1ObdJGw2fShvNMlRndxwmxpkfH9/8gGwyB4BUMHFjSt9pMbVM/U82xBWEW3gnrwQlUt0Q0i7FstT6dhAsqoPLPPJEYGUg6iyBtVhR68UYze4/bt25RHeaCw2O5O8ys1HTb4T4NqK1OIuk2wgPEDvl+SbrNzLZ37+8lrFUWZXRp5/pnEZR4GkytZvbheLwWZzqTMAvtIoszvdjM8upKra5xbeCnhDVmgGuBw83s8TgIn112rpl9VtItZlZopWj3sxxoXZJmmFk+NVmz8lW9u0claYaZGA0cQWeUdzwDDauo3KcOtFWZCjPadlJuNVt7PILq198qHOgEKs7Cmg2Grr5Tgd8TZmgQ1ipPBd5IGJBbDcBTmxzLWzRaUbkudSbGsqp396gkzTATowY1Ku/cZW0o75hZPqlw6expEPrUkbba6FPpzEXSI4QBSQWHzQo0SfMPH2b2mDtW5fr/AbwDOA5YlfC57BzXZduahUm6jDAY1nJjvg842Mze6MrMNLPtcvXMNLPtJN1qLeJw25k9t6LNmbg3t9diLH9gZve10V6DlSDRSBowEwlHK/OfK1fJdNuhPi2ztmJ7pwMnDmRGqyayd/mHjyZ1VDK1SrrfzDYuqeMBM9vIvS8dDN37ywkzyrPiroOAD5rZGyTd2MpTWtIaBMec2RTMnttxtIp1XUBYpy6sSzHGsmqdLdpr+UAwmklesolEI1U8Ldvx3Bwwy7KtyCuBGyQ9KOl2SXdIur3NOs4gC/P4l5mtT9A0beeH/QRgjpnNM7Pe6IV6OmEAOcaVm66go9qAirPDPCfpfZK64+t9BI9Tz4cI6jdPAf8B3gUcCtXCiixoCdfEJB5hAF7JFT2cf14rL+mGPpUkOkZaw0wkGqkUO9eG5+aAWZZtRTqScouBSxFWlfQ7gurroR8irGH+iDCoXU8cDF39jxJEELKLkb4PfL6NvqtDXsm1PjWrq1KMZSc9rkcracBMJGgro0eNTsqetWJZttWO41PTahh44P7UJsfqDi/WRnaYAQyG765QpqGpNsoOFK8BXNuuD6Jm9nz820lhjlFJMskmEoETqGb+q1HJdNshlmVbSNpX0v0Ep5GrCWbAS9s4fyOC5N1AA/fbMbViZlea2U/jq89g2YQq4uNFTk5DhVqM5XSCmMKt8X1tn2dZCXOMSNIMM5EItJOIGZatkPuybAtazGgrmPZOoLrsXTOOoLOSfmUIQOWKN8INmEPNtGnt5R/tZFLrUUfykk0kqO5pWdVzs0N9WmZt5dqdbmY7RY/h7S3orc4yJ2XXIvSkY4H78ZyWoScV6mg2GM4ys7UlPUwwpbYMmenPdSxvJN1KyYPGYHlcjzTSDDORCFRJxAydFU1vxbJsyzPQlFtTm9TdbuB+pyT9ZlA+GC6O7axfpaI4U1tmYhIdRB1anx61pBlmIkG1eLdYrqOzpxZ9WmZtxTorz2jVIdm7oYYqSMPFmdoklqGYRCeI/T6GAcTGjnbSDDORoC1Py6lNqml79tSCZdkWtDej7ZTs3VCjijSc6EzozfJgmXpcjzTSgJlIOCqY/6qabjvBsmwL2nB8ambaayfMYwhSxRvWhpppsw1HpDEDjI0d1SSTbCLRBlVNt8OtrdheOxJzlTR3hxtVpOGGqmmzipleLXR5E81JcZiJRBtUlCobdm1F2ol7HKjs3ZBF0itaFFnM0Lz+0hhLtZGIOlFOmmEmEgmgvRltldCToUYVs6WkG4EVKszUhtz1t3DE+gthffqO3DlbA982s8HyuB5RpDXMRCIBtL32uKzFFAZMVWk4SadXCBkZitffzBGpXWGORAFphplIJCqzvMQUOoUqpC5rMVMb8tefd0Qys8faWZ9OlJMGzEQiUZnhbtprNhi6MqX5R4fy9TdzxBrOsbFDiTRgJhKJyixrMYVO02wwLChbNFMbstevJsnPl7XH9UglrWEmEol2mNrk2GCIKXSUKvGTZTM1Qv7RqU2qX97XX5p/dJjHxg4ZUlhJIpFoh7ZSbg01VC11WbOQkaF8/XlHpB+Tc0Sy/qdAS5BMsolEog2Gu2mvmdnSlSkNGRmK1z8cHJFGCmnATCQSbaMOpNxaHlSJn6yihjOUrn8oOyKNNNKAmUgkRg3NBsPhOlMbyo5II420hplIJEY8FaXhTgDmmNk8M+s1s6VmdjrBBHvMsu91ZaY2Oba8HZFGFGnATCQSo4ETaD0YlqrhAOstq472g6HsiDSiSGEliURiNFBFGm5qk/OH8kztCIZv/tFhRRowE4nEaGBqk2O1wXBZ5x/tCCnGctmRnH4SicSIp4o03FAMGUkMLdKAmUgkRjxtpi4bMiEjiaFFGjATicSoIQ2GiYGQBsxEIpFIJCqQwkoSiUQikahAGjATiUQikahAGjATiUQikahAGjATiUQikahAGjATiUQikajA/wdzyXkAW4IztQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train_corr = pd.concat([X_train, y_train], axis = 1)\n",
    "corr_value = df_train_corr.corr().abs()\n",
    "sns.heatmap(corr_value,square=True,vmax=1,vmin=-1,center=0.0,cmap='coolwarm')\n",
    "corr_value.sort_values([\"Car_Price\"], ascending = False, inplace = True)\n",
    "corr_value[\"Car_Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Brand_Aston</th>\n",
       "      <th>Car_Brand_Audi</th>\n",
       "      <th>Car_Brand_BMW</th>\n",
       "      <th>Car_Brand_Bentley</th>\n",
       "      <th>...</th>\n",
       "      <th>Car_Type_Ute</th>\n",
       "      <th>Car_Type_Van</th>\n",
       "      <th>Car_Gear_Automatic</th>\n",
       "      <th>Car_Gear_Manual</th>\n",
       "      <th>Car_Gear_Tiptronic</th>\n",
       "      <th>Car_Fuel_Type_Diesel</th>\n",
       "      <th>Car_Fuel_Type_Electric</th>\n",
       "      <th>Car_Fuel_Type_Hybrid</th>\n",
       "      <th>Car_Fuel_Type_LPG</th>\n",
       "      <th>Car_Fuel_Type_Petrol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2005</td>\n",
       "      <td>138150</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1500</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>100300</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1800</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2007</td>\n",
       "      <td>102425</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2006</td>\n",
       "      <td>109425</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>125384</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9162</th>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>20000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9163</th>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>167259</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2260</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9164</th>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>43000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9169</th>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>127544</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9170</th>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>97456</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1800</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7045 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Car_ORC  Car_Year  Car_Mileage(km)  Car_Seat  Car_Fuel(cc)  \\\n",
       "0           0      2005           138150       5.0          1500   \n",
       "1           0      2009           100300       7.0          1800   \n",
       "2           1      2007           102425       5.0          2400   \n",
       "3           1      2006           109425       5.0          2400   \n",
       "5           1      2010           125384       5.0          2000   \n",
       "...       ...       ...              ...       ...           ...   \n",
       "9162        1      2019            20000       7.0          3000   \n",
       "9163        1      2014           167259       5.0          2260   \n",
       "9164        1      2013            43000       5.0          1600   \n",
       "9169        1      2011           127544       7.0          2000   \n",
       "9170        1      2014            97456       5.0          1800   \n",
       "\n",
       "      Car_Door_Num  Car_Brand_Aston  Car_Brand_Audi  Car_Brand_BMW  \\\n",
       "0              5.0                0               0              0   \n",
       "1              5.0                0               0              0   \n",
       "2              5.0                0               0              0   \n",
       "3              5.0                0               0              0   \n",
       "5              5.0                0               0              0   \n",
       "...            ...              ...             ...            ...   \n",
       "9162           5.0                0               0              0   \n",
       "9163           4.0                0               0              0   \n",
       "9164           5.0                0               0              0   \n",
       "9169           0.0                0               0              0   \n",
       "9170           5.0                0               0              0   \n",
       "\n",
       "      Car_Brand_Bentley  ...  Car_Type_Ute  Car_Type_Van  Car_Gear_Automatic  \\\n",
       "0                     0  ...             0             0                   1   \n",
       "1                     0  ...             0             0                   1   \n",
       "2                     0  ...             0             0                   1   \n",
       "3                     0  ...             0             0                   1   \n",
       "5                     0  ...             0             0                   1   \n",
       "...                 ...  ...           ...           ...                 ...   \n",
       "9162                  0  ...             0             0                   0   \n",
       "9163                  0  ...             0             0                   1   \n",
       "9164                  0  ...             0             0                   1   \n",
       "9169                  0  ...             0             1                   1   \n",
       "9170                  0  ...             0             1                   1   \n",
       "\n",
       "      Car_Gear_Manual  Car_Gear_Tiptronic  Car_Fuel_Type_Diesel  \\\n",
       "0                   0                   0                     0   \n",
       "1                   0                   0                     0   \n",
       "2                   0                   0                     0   \n",
       "3                   0                   0                     0   \n",
       "5                   0                   0                     0   \n",
       "...               ...                 ...                   ...   \n",
       "9162                0                   1                     1   \n",
       "9163                0                   0                     0   \n",
       "9164                0                   0                     0   \n",
       "9169                0                   0                     0   \n",
       "9170                0                   0                     0   \n",
       "\n",
       "      Car_Fuel_Type_Electric  Car_Fuel_Type_Hybrid  Car_Fuel_Type_LPG  \\\n",
       "0                          0                     0                  0   \n",
       "1                          0                     0                  0   \n",
       "2                          0                     0                  0   \n",
       "3                          0                     0                  0   \n",
       "5                          0                     0                  0   \n",
       "...                      ...                   ...                ...   \n",
       "9162                       0                     0                  0   \n",
       "9163                       0                     0                  0   \n",
       "9164                       0                     0                  0   \n",
       "9169                       0                     0                  0   \n",
       "9170                       0                     0                  0   \n",
       "\n",
       "      Car_Fuel_Type_Petrol  \n",
       "0                        1  \n",
       "1                        1  \n",
       "2                        1  \n",
       "3                        1  \n",
       "5                        1  \n",
       "...                    ...  \n",
       "9162                     0  \n",
       "9163                     1  \n",
       "9164                     1  \n",
       "9169                     1  \n",
       "9170                     1  \n",
       "\n",
       "[7045 rows x 70 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         3997\n",
       "1         7997\n",
       "2         9940\n",
       "3         8940\n",
       "5         9400\n",
       "         ...  \n",
       "9162    115995\n",
       "9163     13850\n",
       "9164     20980\n",
       "9169      6400\n",
       "9170     11500\n",
       "Name: Car_Price, Length: 7045, dtype: int32"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression().fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "162435710.38437897"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler,MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "mm =  MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = mm.fit_transform(X_train)\n",
    "x_test = mm.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_s = LinearRegression().fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_s = lr_s.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11712., 10432., 21312., ..., 17536., 16000., 17472.])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9171      4900\n",
       "9175      7400\n",
       "9176      8900\n",
       "9178      9900\n",
       "9180      5800\n",
       "         ...  \n",
       "13434    14990\n",
       "13435     7995\n",
       "13436    14450\n",
       "13437    12800\n",
       "13438     4999\n",
       "Name: Car_Price, Length: 3020, dtype: int32"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1897895152495478e+31"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_pred_s,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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